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  • Articles  (36,379)
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  • 1
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    An Adaptive-MSSA-Based Algorithm for Detection of Trapped Victims Using UWB Radar (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: Postdisaster search and rescue is an important application of ultrawideband (UWB) radar systems, which mainly detect trapped victims by their respiratory-motion response. The development of a respiratory-motion detection (RMD) algorithm that can eliminate nonstationary clutter and noise is a challenging task for the application. A new algorithm is proposed to deal with the task in this letter. It uses the multichannel singular spectrum analysis (MSSA) technique to reconstruct the respiratory-motion response detected by a UWB radar. During the reconstruction, the periodicity and range interrelation characteristics of the response are exploited to adaptively identify signal subspaces. The performance of the algorithm is verified both by simulated and real data. The results show its improved performance over the reference algorithms, e.g., a singular-value-decomposition-based algorithm. The adaptive-MSSA-based RMD algorithm has great promise not only in practical use but also for future research of UWB-radar-based human being remote sensing.
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  • 2
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    Impact of Correlator Efficiency Errors on SMOS Land–Sea Contamination (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: Land–sea contamination observed in Soil Moisture and Ocean Salinity (SMOS) brightness temperature images is found to have two main contributions: the floor error inherent of image reconstruction and a multiplicative error either in the antenna temperature or in the visibility samples measured by the correlator. The origin of this last one is traced down to SMOS calibration parameters to yield a simple correction scheme, which is validated against several geophysical scenarios. Autoconsistency rules in interferometric synthesis together with redundant and complementary calibration procedures provide a robust SMOS calibration scheme.
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  • 3
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    An Estimation Algorithm for Phase Errors in Synthetic Aperture Radar Imagery (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: This letter proposes a novel algorithm, which is based on the generalized method of moments (GMM), for the estimation and correction of phase errors induced in synthetic aperture radar (SAR) imagery. The GMM algorithm is used to replace the original phase-estimation kernel in the basic structure of the phase-gradient-autofocus algorithm. Since this novel algorithm does not require the observed signal to be a certain distribution model, it is able to estimate arbitrary phase errors. The GMM algorithm has the ability of estimating range-dependent phase errors, which makes it an efficient estimator. As a result, higher accuracy of the estimated phase errors and a better focused image can be achieved. Excellent results have been obtained in autofocusing and imaging experiments on real SAR data.
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  • 4
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    Efficient Clutter Suppression in SAR Images With Shedding Irrelevant Patterns (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: Strong clutter reflections of terrain and marine surfaces obscure the contrast between the target-of-interest and clutter (terrain and marine surface reflections) in synthetic aperture radar (SAR) images and consequently hinder the efficiency of image interpretation and analysis. To overcome this problem, this letter proposes an efficient clutter suppression method in SAR images, which is named shedding irrelevant patterns (SIP). The essence is to construct a regression function that can suppress clutter and preserve the target patterns concurrently. We assume that the clutter is irrelevant to the target-of-interest and distinguishable in patterns in terms of image-pixel distribution and intensity (spatial information). Experimental results show the efficiency of the proposed method in both clutter suppression and target pattern preservation.
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  • 5
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    Classification of Hyperspectral Images Using Subspace Projection Feature Space (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: A concern in hyperspectral image classification is the high number of required training samples. When traditional classifiers are applied, feature reduction (FR) techniques are the most common approaches to deal with this problem. Subspace-based classifiers, which are developed based on high-dimensional space characteristics, are another way to handle the high dimension of hyperspectral images. In this letter, a novel subspace-based classification approach is proposed and compared with basic and improved subspace-based classifiers. The proposed classifier is also compared with traditional classifiers that are accompanied by an FR technique and the well-known support vector machine classifier. Experimental results prove the efficiency of the proposed method, especially when a limited number of training samples are available. Furthermore, the proposed method has a very high level of automation and simplicity, as it has no parameters to be set.
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  • 6
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    Mathematical Framework for Phase-Triangulation Algorithms in Distributed-Scatterer Interferometry (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: To improve the spatial density of measurement points of persistent-scatterer interferometry, distributed scatterer (DS) should be considered and processed. An important procedure in DS interferometry is the phase triangulation (PT). This letter introduces two modified PT algorithms (i.e., equal-weighted PT and coherence-weighted PT) and analyzes the mathematical relations between different published PT methods (i.e., the maximum-likelihood phase estimator, least squares estimator, and eigendecomposition-based phase estimators). The analysis shows that the above five PT methods share very similar mathematical forms with different weight values in the estimation procedure.
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  • 7
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    Hyperspectral Image Classification via Sparse Code Histogram (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: Sparse representation-based classifier and its variants have been widely adopted for hyperspectral image (HSI) classification recently. However, sparse representation is unstable so that similar features might obtain significantly different sparse codes. Despite the instability, we find that the sparse codes follow a class-dependent distribution under the structured dictionary consisting of training samples from all classes. Based on this observation, a novel discriminative feature, sparse code histogram (SCH), is developed for HSI classification. By counting the SCH of each sample from the sparse codes of its spatial neighbors, we can statistically obtain the distribution pattern of sparse codes of the class to which the sample belongs, and then treat the SCH as a new feature for classification. To reduce the possible outliers among the neighbors, a shape-adaptive neighborhood extractor is also employed to enhance the stability of the histogram feature. Experimental results demonstrate that SCH enjoys a strong discriminative power, which can achieve notably better performance than several state-of-the-art methods for HSI classification with limited training samples.
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  • 8
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    A Generalized Likelihood Ratio Test for Coherent Change Detection in Polarimetric SAR (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: Several detection statistics have been proposed for detecting fine ground disturbances between two synthetic aperture radar (SAR) images, such as vehicle tracks. The standard method involves estimating a local correlation coefficient between images. Other methods have been proposed using various statistical hypothesis tests. One of these alternative methods is a generalized likelihood ratio test (GLRT), which compares a full-correlation image model to a no-correlation image model. In this letter, we expand the GLRT to polarimetric SAR data and derive the appropriate GLRT detection statistics. Additionally, we explore relaxing the equal variance/equal polarimetric covariance assumptions used in previous results and find improved performance on macroscopic scene changes.
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  • 9
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    Truncated SVD-Based Compressive Sensing for Downward-Looking Three-Dimensional SAR Imaging With Uniform/Nonuniform Linear Array (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: For downward-looking linear array 3-D synthetic aperture radar, the resolution in cross-track direction is much lower than the ones in range and azimuth. Hence, superresolution reconstruction algorithms are desired. Since the cross-track signal to be reconstructed is sparse in the object domain, compressive sensing algorithm has been used. However, the imaging processing on the 3-D scene brings large computational loads, which renders challenges in both data acquisition and processing. To cover this shortage, truncated singular value decomposition is utilized to reconstruct a reduced-redundancy spatial measurement matrix. The proposed algorithm provides advantages in terms of computational time while maintaining the quality of the scene reconstructions. Moreover, our results on uniform linear array are generally applicable to sparse nonuniform linear array. Superresolution properties and reconstruction accuracies are demonstrated using simulations under the noise and clutter scenarios.
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  • 10
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    An Experimental Study of Passive Bistatic Radar Using Uncooperative Radar as a Transmitter (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: This letter proposes a signal processing method of passive bistatic radar (PBR) exploiting an uncooperative radar as an illuminator. Compared with other opportunity illuminators, the transmitting signal of a radar usually has a better ambiguity function, which leads to a higher range resolution. Two channels are needed in PBR system. The reference channel is used to estimate radar signal parameters and reconstruct directly propagated signal. The surveillance channel is used to receive scattered wave. An array antenna and a simultaneous multibeam algorithm are necessary in the surveillance channel due to the flexible beam scanning of the uncooperative radar. The procedure of the proposed method is explained in detail, which is then followed by a field experiment. Preliminary results from the field experiment show that the proposed method can be applied to target angle and bistatic range measurement successfully.
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  • 11
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    Statistical Analysis of the Effects of Virtual Element Position Errors on Airborne Down-Looking LASAR 3-D Imaging (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: In order to achieve 3-D imaging with an airborne down-looking linear-array synthetic aperture radar (LASAR), a uniform virtual antenna array may be obtained by aperture synthesis of the cross-track sparse multiple-input–multiple-output array. However, the actual 3-D imaging quality is unavoidably degraded by errors in the virtual element position. In this letter, we investigate the effects of these errors on the forms and the degrees of image quality degradation by decomposing the error-related stochastic processes via an orthogonal transform based on discrete Legendre polynomials. It should be noted that these analyses are helpful for designing a LASAR system and providing a reference for specifying the requisite precision of measurement devices and calibration methods. Finally, we briefly consider the use of calibration methods to eliminate the effects of errors.
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  • 12
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    Semisupervised Hyperspectral Image Classification via Neighborhood Graph Learning (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: In problems where labeled data are scarce, semisupervised learning (SSL) techniques are an attractive framework that can exploit both labeled and unlabeled data. These approaches typically rely on a smoothness assumption such that examples that are similar in input space should also be similar in label space. In many domains, such as remotely sensed hyperspectral image (HSI) classification, the data violate this assumption. In response, we propose a general method by which a neighborhood graph used in SSL is learned using binary classifiers that are trained to predict whether a pair of pixels shares the same label. Working within the framework of semisupervised neural networks (SSNNs), we show that our approach improves on the performance of the SSNN on two HSI data sets.
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  • 13
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    New Spatial Correlation Models for Sea Clutter (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: In this letter, new models for the spatial correlation of sea clutter texture and intensity are proposed as improved versions of current power law models or exponential decay model. The models for texture have three unknown parameters, and thus can be called triparametric models. The structure of the models is a weighted sum of two components, which can describe the decaying process of the correlation coefficient with spatial lags, as well as the periodic behavior due to the existence of transient coherent structures in sea clutter. Unknown parameters are optimized by the nonlinear least square fit method. Models for sea clutter intensity can be obtained through a linear transform for uncorrelated speckle based on the compound-Gaussian representation of sea clutter. The proposed models are validated and compared with current models using S- and C-band measured sea clutter data. Analysis results indicate the effectiveness of the proposed models in that they can describe the behavior of spatial correlation coefficients with higher accuracy.
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  • 14
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    Accuracy of Digital Elevation Models Derived From Terrestrial Laser Scanning Data (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: Terrestrial laser scanning (TLS) has become a popular tool for acquiring source data points which can be used to construct digital elevation models (DEMs) for a wide number of applications. A TLS point cloud often has a very fine spatial resolution, which can represent well the spatial variation of a terrain surface. However, the uncertainty in DEMs created from this relatively new type of source data is not well understood, which forms the focus of this letter. TLS survey data representing four terrain surfaces of different characteristics were used to explore the effects of surface complexity and typical TLS data density (in terms of data point spacing) on DEM accuracy. The spatial variation in TLS data can be decomposed into parts corresponding to the signal of spatial variation (of terrain surfaces) and noise due to measurement error. We found a linear relation between the DEM error and the typical TLS data spacings considered (30–100 mm) which arises as a function of the interpolation error, and a constant contribution from the propagated data noise. This letter quantifies these components for each of the four surfaces considered and shows that, for the interpolation method considered here, higher density sampling would not be beneficial.
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  • 15
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    Estimation of Grassland Live Fuel Moisture Content From Ratio of Canopy Water Content and Foliage Dry Biomass (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: A novel way to estimate the live fuel moisture content (LFMC) was explored from the ratio of canopy water content (CWC) and foliage dry biomass (FDB). The CWC was estimated using the PROSAIL (PROSPECT + SAIL) radiative transfer model from the Landsat 8 product. A weak constraint 4-D variational data assimilation method was employed to assimilate the temporally estimated leaf area index into a soil-water-atmosphere-plant (SWAP) model for optimizing the model control variables. Then, the SWAP model was reinitialized with this optimum set of control variables, and better prediction of FDB was obtained. Results showed that a high accuracy level was achieved for the estimated CWC ( $R^{2}=0.91$ , $mbox{RMSE}=84.74 mbox{g/m}^2$ ) and FDB ( $R^2=0.88$ , $mbox{RMSE}=48.54 mbox{g/m} ^2$ ) when compared with in situ measured values. However, the accuracy level of estimated LFMC was poor ( $R^2=0.59$ , $mbox{RMSE} =30.85%$ ) . Further analyses find that the estimated LFMC is reliable for low LFMC but challenged for high LFMC, which indicates that the presented method still makes sense to the assessment of wildfire risk since the wildfire generally occurs when the vegetation is in low LFMC condition.
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  • 16
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    Classification of Unarmed/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: In this letter, we present the use of experimental human micro-Doppler signature data gathered by a multistatic radar system to discriminate between unarmed and potentially armed personnel walking along different trajectories. Different ways of extracting suitable features from the spectrograms of the micro-Doppler signatures are discussed, particularly empirical features such as Doppler bandwidth, periodicity, and others, and features extracted from singular value decomposition (SVD) vectors. High classification accuracy of armed versus unarmed personnel (between 90% and 97% depending on the walking trajectory of the people) can be achieved with a single SVD-based feature, in comparison with using four empirical features. The impact on classification performance of different aspect angles and the benefit of combining multistatic information is also evaluated in this letter.
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  • 17
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    Accurate Urban Area Detection in Remote Sensing Images (2015)
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    Publication Date: 2015-08-11
    Description: Automatic urban area detection in remote sensing images is an important application in the field of earth observation. Most of the existing methods employ feature classifiers and thereby contain a data training process. Moreover, some methods cannot detect urban areas in complex scenes accurately. This letter proposes an automatic urban area detection method that uses multiple features that have different resolutions. First, a downsampled low-resolution image is used to segment the candidate area. After the corner points of the urban area are extracted, a weighted Gaussian voting matrix technique is employed to integrate the corner points into the candidate area. Then, the edge features and homogeneous region are extracted by using the original high-resolution image. Using these results as the input, the processes of guided filtering and contrast enhancement can finally detect accurately the urban areas. This method combines multiple features, such as corner, edge, and regional characteristics, to detect the urban areas. The experimental results show that the proposed method has better detection accuracy for urban areas than the existing algorithms.
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  • 18
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    Advanced Spectral Analysis and Cross Correlation Based on the Empirical Mode Decomposition: Application to the Environmental Time Series (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: In marine sciences, time series are often nonlinear and nonstationary. Adequate and specific methods are needed to analyze such series. In this letter, an application of the empirical mode decomposition method (EMD) associated to the Hilbert spectral analysis (HSA) is presented. Furthermore, EMD-based time-dependent intrinsic correlation (TDIC) analysis is applied to consider the correlation between two nonstationary time series. Four temperature time series obtained from automatic measurements in nearshore waters of the Réunion island are considered, recorded every 10 min from July 2011 to January 2012. The application of the EMD on these series and the estimation of their power spectra using the HSA are illustrated. The authors identify low-frequency tidal waves and display the pattern of correlations at different scales and different locations. By TDIC analysis, it was concluded that the high-frequency modes have small correlation, whereas the trends are perfectly correlated.
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  • 19
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    Fast Multiclass Vehicle Detection on Aerial Images (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: Detecting vehicles in aerial images provides important information for traffic management and urban planning. Detecting the cars in the images is challenging due to the relatively small size of the target objects and the complex background in man-made areas. It is particularly challenging if the goal is near-real-time detection, i.e., within few seconds, on large images without any additional information, e.g., road database and accurate target size. We present a method that can detect the vehicles on a 21-MPixel original frame image without accurate scale information within seconds on a laptop single threaded. In addition to the bounding box of the vehicles, we extract also orientation and type (car/truck) information. First, we apply a fast binary detector using integral channel features in a soft-cascade structure. In the next step, we apply a multiclass classifier on the output of the binary detector, which gives the orientation and type of the vehicles. We evaluate our method on a challenging data set of original aerial images over Munich and a data set captured from an unmanned aerial vehicle (UAV).
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  • 20
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    Comparison of Five Slope Correction Methods for Leaf Area Index Estimation From Hemispherical Photography (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: We compare five slope correction methods developed by Walter et al. , Montes et al. , Schleppi et al. , España et al. , and Gonsamo et al. (referred to as WAL, MON, SCH, ESP, and GON, respectively) using artificial fisheye pictures simulated by graphics software and a lookup table (LUT) retrieval method. The LUT is built by simulating the directional gap fraction as a function of leaf area index (LAI) and average leaf inclination angle (ALIA) using the Poisson law. LAI and ALIA estimates correspond to the case of the LUT that provides the lowest root-mean-square error between the observed gap fractions after slope correction and the simulated ones. Three LAI values (1.5, 3.5, and 5.5), four ALIA values (26.8°, 45°, 57.5°, and 63.2°), and three slope angles (0°, 20°, and 50°) constituted 36 samples of random scenes. ESP is recommended because its results are accurate and independent on the leaf angle distribution (LAD), while GON only performs well for spherical LAD. The three other methods present less good performances with underestimation or overestimation of LAI and/or ALIA depending on the LAD, and the recommended order for them is MON, SCH, and WAL.
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  • 21
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    An Improved Phase Correlation Method Based on 2-D Plane Fitting and the Maximum Kernel Density Estimator (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.
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  • 22
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    Improvement of Forest Carbon Estimation by Integration of Regression Modeling and Spectral Unmixing of Landsat Data (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: Accurately mapping forest carbon density by combining sample plots and remotely sensed images has become popular because this method provides spatially explicit estimates. However, mixed pixels often impede the improvement of the estimation. In this letter, regression modeling and spectral unmixing analysis were integrated to improve the estimation of forest carbon density for the You County of Hunan, China, using Landsat Thematic Mapper images. Linear spectral unmixing with and without a constraint (LSUWC and LSUWOC) and nonlinear spectral unmixing (NSU) were compared to derive the fractions of five endmembers, particularly forests. Stepwise regression, logistic regression, and polynomial regression (PR) with and without the forest fraction used as an independent variable and the product of the forest fraction image and the map from the best model without the forest fraction were compared. The models were developed using 56 sample plots, and their results were validated using 26 test plots. The decomposition of mixed pixels was assessed using higher spatial resolution SPOT images and a corresponding land cover map. The results showed that 1) LSUWC more accurately estimated the endmember fractions than LSUWOC and NSU, 2) PR had the greatest estimation accuracy of forest carbon, and 3) combining regression modeling and spectral unmixing increased the estimation accuracy by 31%–39%, and introducing the forest fraction into the regressions performed better than the product of forest fraction image and the results from PR without the fraction. This implied that the integrations provided great potential in reducing the impacts of mixed pixels in mapping forest carbon.
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  • 23
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    Reliable and Fast Mapping of Keypoints on Large-Size Remote Sensing Images by Use of Multiresolution and Global Information (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: This letter proposes a multiresolution technique to address the high computational cost in remote sensing image registration. The scale-invariant feature transform is applied to detect keypoints and descriptors, and then, global information combined with descriptors is utilized to establish keypoint mappings. Keypoints are first classified according to their octaves. Then, in the lowest resolution, the keypoints of the largest octave are mapped with descriptors and the global information, giving an initial affine transformation $T_0$ . In the next octave, the keypoints of the second largest octave are mapped by employing $T_0$ to narrow the space of matching keypoints. By this means, the process of establishing keypoint correspondences is conducted from one resolution (octave) to the next as the obtained transformation gets finer until we get to the highest resolution. Due to the high computational expense of computing global information, the proposed technique is important for aligning large-size remote sensing imagery. Experimental results show that the proposed method can achieve a comparable registration accuracy but with a less computational cost than directly building keypoint mappings on images of large size.
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    GPU Implementation of Composite Kernels for Hyperspectral Image Classification (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: In this letter, we present an efficient parallel implementation of composite kernels in support vector machines (SVMs) for hyperspectral image (HSI) classification. Our implementation makes effective use of commodity graphics processing units (GPUs). Specifically, we port the calculation of composite kernels to GPUs, perform intensive computations based on NVidia's compute unified device architecture, and execute the rest of the operations related with control and small data calculations in the CPU. Our experimental results, conducted using real hyperspectral data sets and NVidia GPU platforms, indicate significant improvements in terms of computational effectiveness, achieving near-real-time performance of spatial–spectral HSI classification for the first time in the literature.
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    Retrieval and Accuracy Assessment of Tree and Stand Parameters for Chinese Fir Plantation Using Terrestrial Laser Scanning (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: Compared with airborne laser scanning, terrestrial laser scanning (TLS) offers ground-based point cloud data of trees and provides greater potential to accurately estimate tree and stand parameters. However, there is a lack of effective methods to accurately identify locations of individual trees from TLS point cloud data. It is also unknown whether the estimation accuracy of the parameters, including tree height (H), diameter at breast height (DBH), and so on, using TLS can meet the requirement of forest management and planning. In this letter, a novel method to effectively process point cloud data and further determine the locations of individual trees in a stand based on the central coordinates of point cloud data on a defined grid according to the largest DBH was developed. Moreover, a point-cloud-data-based convex hull algorithm and the cylinder method were, respectively, used to estimate DBH and H of individual trees. This study was conducted in a pure Chinese fir plantation of 45 trees located in Huang-Feng-Qiao forest farm, You County of Hunan, China. The comparison of the estimated and observed values showed that the obtained tree locations had errors of less than 20 cm, and the relative root mean square errors for the estimates of both DBH and H were less than 5%. This implies that TLS is very promising for the retrieval of tree and stand parameters in forest stands. For the applications of these methods to mixed forests with a structure of multilayer canopies, further examination is needed.
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    Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization (2015)
    Hindawi
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    Publication Date: 2015-08-11
    Description: A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO) for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection) is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.
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    Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine (2015)
    Hindawi
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    Publication Date: 2015-08-11
    Description: For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.
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    A 3-D Electromagnetic-Model-Based Algorithm for Absolute Attitude Measurement Using Wideband Radar (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: In this letter, a novel algorithm for attitude measurement based on a 3-D electromagnetic model (3-D em-model) is proposed. The 3-D em-model is established offline based on the geometric structure of the target, and it can be used to predict the scattering features at different target attitudes. In order to measure the attitude of the air target, we design a bistatic step frequency radar system. The directions of the two radars' lines of sight (LOSs) relative to the target are acquired by matching the high-resolution range profiles (HRRPs) from the target echoes to the HRRPs generated from the 3-D em-model. Since the directions of two radars' LOSs relative to the Earth are already known, the absolute attitude of the target can be acquired. The innovative contributions of this letter are as follows: 1) A comprehensive theoretical analysis of air target attitude measurement based on its own 3-D em-model is proposed; 2) the method can be applied to different kinds of air targets such as aircraft, satellite, missile, etc.; 3) the proposed attitude measurement method does not require target motion model in advance; and 4) the proposed algorithm can be applied to any kind of step frequency waveforms. Experiments using both data predicted by a high-frequency electromagnetic code and data measured in the chamber verify the validity of the method.
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    Converting Between SMOS and SMAP Level-1 Brightness Temperature Observations Over Nonfrozen Land (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions provide Level-1 brightness temperature (Tb) observations that are used for global soil moisture estimation. However, the nature of these Tb data differs: the SMOS Tb observations contain atmospheric and select reflected extraterrestrial (“Sky”) radiation, whereas the SMAP Tb data are corrected for these contributions, using auxiliary near-surface information. Furthermore, the SMOS Tb observations are multiangular, whereas the SMAP Tb is measured at 40° incidence angle only. This letter discusses how SMOS Tb, SMAP Tb, and radiative transfer modeling components can be aligned in order to enable a seamless exchange of SMOS and SMAP Tb data in soil moisture retrieval and assimilation systems. The aggregated contribution of the atmospheric and reflected Sky radiation is, on average, about 1 K for horizontally polarized Tb and 0.5 K for vertically polarized Tb at 40° incidence angle, but local and short-term values regularly exceed 5 K.
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    Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine (2015)
    Hindawi
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    Publication Date: 2015-08-12
    Description: Receptor tyrosine kinases are essential proteins involved in cellular differentiation and proliferation in vivo and are heavily involved in allergic diseases, diabetes, and onset/proliferation of cancerous cells. Identifying the interacting partner of this protein, a growth factor ligand, will provide a deeper understanding of cellular proliferation/differentiation and other cell processes. In this study, we developed a method for predicting tyrosine kinase ligand-receptor pairs from their amino acid sequences. We collected tyrosine kinase ligand-receptor pairs from the Database of Interacting Proteins (DIP) and UniProtKB, filtered them by removing sequence redundancy, and used them as a dataset for machine learning and assessment of predictive performance. Our prediction method is based on support vector machines (SVMs), and we evaluated several input features suitable for tyrosine kinase for machine learning and compared and analyzed the results. Using sequence pattern information and domain information extracted from sequences as input features, we obtained 0.996 of the area under the receiver operating characteristic curve. This accuracy is higher than that obtained from general protein-protein interaction pair predictions.
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    Electronic ISSN: 1687-8035
    Topics: Biology , Computer Science
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    Experimental and Theoretical Investigations on Frost Heave in Porous Media (2015)
    Hindawi
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    Publication Date: 2015-08-12
    Description: For the construction in cold regions, frost heave and thaw settlement are the two factors which must be taken care of. Considered that a saturated soil column was subjected to an overburden pressure to model the ice lens growing process. A typical process, which coupled water, heat, and stress that happened in a saturated freezing soil column, was simulated by the finite element software. We did the numerical simulation under the same conditions as the experiment tests and then compared the results from temperature, frost heave, frozen structure, water content, and water intake. Result shows that the simulation results match well with the experimental results, and the correctness of the mathematical model is validated. On that basis, frost heave amount under different conditions by changing the temperature boundary and loading boundary is obtained. The frost heave has an optimum temperature gradient. Under the optimum value, the frost heave amount increases with increasing temperature gradient. Above the optimum value, frost heave decreases with increasing temperature gradient. Increasing the overburden pressure, frost heave amount always decreases. These results can provide references for the constrictions in cold regions.
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    An Unconditionally Stable Method for Solving the Acoustic Wave Equation (2015)
    Hindawi
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    Publication Date: 2015-08-12
    Description: An unconditionally stable method for solving the time-domain acoustic wave equation using Associated Hermit orthogonal functions is proposed. The second-order time derivatives in acoustic wave equation are expanded by these orthogonal basis functions. By applying Galerkin temporal testing procedure, the time variable can be eliminated from the calculations. The restriction of Courant-Friedrichs-Levy (CFL) condition in selecting time step for analyzing thin layer can be avoided. Numerical results show the accuracy and the efficiency of the proposed method.
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    Complete Bilateral Teleoperation System for a Rotorcraft UAV with Time-Varying Delay (2015)
    Hindawi
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    Publication Date: 2015-08-12
    Description: This paper proposes the use of simple proportional plus damping injection (P+d) controllers for delayed bilateral teleoperation of a rotorcraft UAV. The proposed control scheme involves P+d remote and local controllers, considers master and slave dynamics, and takes into account asymmetric time-varying delays. The stability of the proposed teleoperation system is analyzed using Lyapunov-Krasovskii functionals and delay-dependent stability criteria are obtained under linear-matrix-inequalities conditions. The performance of the teleoperation scheme is tested driving a virtual nonlinear 6DOF dynamic model of a minihelicopter in a human-in-the-loop simulation.
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    Application of the Periodic Average System Model in Dam Deformation Analysis (2015)
    Hindawi
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    Publication Date: 2015-08-12
    Description: Dams are among the most important hydraulic engineering facilities used for water supply, flood control, and hydroelectric power. Monitoring of dams is crucial since deformation might have occurred. How to obtain the deformation information and then judge the safe conditions is the key and difficult problem in dam deformation monitoring field. This paper proposes the periodic average system model and creates the concept of “settlement activity” based on the dam deformation issue. Long-term deformation monitoring data is carried out in a pumped-storage power station, this model combined with settlement activity is used to make the single point deformation analysis, and then the whole settlement activity profile is drawn by clustering analysis. Considering the cumulative settlement value of every point, the dam deformation trend is analyzed in an intuitive effect way. The analysis mode of combined single point with multipoints is realized. The results show that the key deformation information of the dam can be easily grasped by the application of the periodic average system model combined with the distribution diagram of settlement activity. And, above all, the ideas of this research provide an effective method for dam deformation analysis.
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    The Heat Transfer Coefficient of Recycled Concrete Bricks Combination with EPS Insulation Board Wall (2015)
    Hindawi
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    Publication Date: 2015-08-12
    Description: Four tectonic forms samples were conducted to test their heat transfer coefficients. By analyzing and comparing the test values and theoretical values of the heat transfer coefficient, a corrected-value calculation method for determining the heat transfer coefficient was proposed; the proposed method was proved to be reasonably correct. The results indicated that the recycled concrete brick wall heat transfer coefficient is higher than that of the clay brick wall, the heat transfer coefficient of recycled concrete brick wall could be effectively reduced when combined with the EPS insulation board, and the sandwich insulation type was better than that of external thermal insulation type.
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    Four-Channel Teleoperation with Time-Varying Delays and Disturbance Observers (2015)
    Hindawi
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    Publication Date: 2015-08-13
    Description: This paper addresses the robust stability of teleoperated systems under the four-channel architecture, affected by time-varying communication delays and using disturbance observers. It is based on our previous work which provides a framework for robust stability against delays with bounded variation and a bounded time-derivative, using structured singular values (SSV). The main new feature here is the inclusion of disturbance observers (DOBs). The DOB concept is well-documented and relevant to many applications, since only position (but not force) measurements are usually available. In this paper, we adapt two DOBs (master and slave) to our generic framework, by representing them as stable, fast filters affected by the uncertainty in the plant modelling. Our main result is an SSV test to verify robust stability. The simulation results confirm the usefulness of this approach.
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    Hom--Operators and Hom-Yang-Baxter Equations (2015)
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    Publication Date: 2015-08-13
    Description: In Hom-Lie set, we introduce the concept of Hom--operators and study its relation with classical Hom-Yang-Baxter equation, as well as left-symmetric Hom-algebras. We construct the corresponding relation between left-symmetric Hom-algebras and Hom-1-cocycles, which are both related to classical Hom-Yang-Baxter equation. Moreover, in Hom-algebra setting, we establish the equivalent relationship between AHYBE (associative Hom-Yang-Baxter equations) and -operators on Frobenius monoidal Hom-algebras.
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    Heat and Mass Transfer for MHD Viscoelastic Fluid Flow over a Vertical Stretching Sheet with Considering Soret and Dufour Effects (2015)
    Hindawi
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    Publication Date: 2015-08-13
    Description: The homotopy analysis method (HAM) with two auxiliary parameters is employed to examine heat and mass transfer in a steady two-dimensional magneto hydrodynamic viscoelastic fluid flow over a stretching vertical surface by considering Soret and Dufour effects. The two-dimensional boundary-layer governing partial differential equations are derived by considering the Boussinesq approximation. The highly nonlinear ordinary differential forms of momentum, energy, and concentration equations are obtained by similarity transformation. These equations are solved analytically in the presence of buoyancy force. The effects of different involved parameters such as magnetic field parameter, Prandtl number, buoyancy parameter, Soret number, Dufour number, and Lewis number on velocity, temperature, and concentration profiles are plotted and discussed. The effect of the second auxiliary parameter is also illustrated. Results show that the effect of increasing Soret number or decreasing Dufour number tends to decrease the velocity and temperature profiles (increase in Sr cools the fluid and reduces the temperature) while enhancing the concentration distribution.
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    Application of the Preview Control Method to the Optimal Tracking Control Problem for Continuous-Time Systems with Time-Delay (2015)
    Hindawi
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    Publication Date: 2015-08-13
    Description: This paper considers the application of the preview control method to the optimal tracking control problem for a class of continuous-time systems with state and input delays. First, through a transformation, the system is transformed into a nondelayed one. Then, the tracking problem of the time-delay system is transformed into one of a nondelayed system via processing of the reference signal. We then apply preview control theory to derive an augmented system for the nondelayed system and design a controller with preview function assuming that the reference signal is previewable. Finally, we obtain the optimal control law of the augmented error system and thus obtain that of the original system by letting the preview length of the reference signal go to zero. Numerical simulations are provided to illustrate the effectiveness and validity of our conclusions.
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    Rotation and Magnetic Field Effect on Surface Waves Propagation in an Elastic Layer Lying over a Generalized Thermoelastic Diffusive Half-Space with Imperfect Boundary (2015)
    Hindawi
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    Publication Date: 2015-08-13
    Description: The aim of the present investigation is to study the effects of magnetic field, relaxation times, and rotation on the propagation of surface waves with imperfect boundary. The propagation between an isotropic elastic layer of finite thickness and a homogenous isotropic thermodiffusive elastic half-space with rotation in the context of Green-Lindsay (GL) model is studied. The secular equation for surface waves in compact form is derived after developing the mathematical model. The phase velocity and attenuation coefficient are obtained for stiffness, and then deduced for normal stiffness, tangential stiffness and welded contact. The amplitudes of displacements, temperature, and concentration are computed analytically at the free plane boundary. Some special cases are illustrated and compared with previous results obtained by other authors. The effects of rotation, magnetic field, and relaxation times on the speed, attenuation coefficient, and the amplitudes of displacements, temperature, and concentration are displayed graphically.
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    Numerical Investigation of Non-Newtonian Flow and Heat Transfer Characteristics in Rectangular Tubes with Protrusions (2015)
    Hindawi
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    Publication Date: 2015-08-13
    Description: Flow characteristics and heat transfer performances in rectangular tubes with protrusions are numerically investigated in this paper. The thermal heat transfer enhancement of composite structures and flow resistance reduction of non-Newtonian fluid are taken advantage of to obtain a better thermal performance. Protrusion channels coupled with different CMC concentration solutions are studied, and the results are compared with that of smooth channels with water flow. The comprehensive influence of turbulence effects, structural effects, and secondary flow effects on the CMC’s flow in protrusion tubes is extensively investigated. The results indicate that the variation of flow resistance parameters of shear-thinning power-law fluid often shows a nonmonotonic trend, which is different from that of water. It can be concluded that protrusion structure can effectively enhance the heat transfer of CMC solution with low pressure penalty in specific cases. Moreover, for a specific protrusion structure and a fixed flow velocity, there exists an optimal solution concentration showing the best thermal performance.
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    Research on Conflict Behavior of Top Management Team in Family Enterprises: A Complex Network Perspective (2015)
    Hindawi
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    Publication Date: 2015-08-13
    Description: At present time, methods of research about top management team’s conflict become more and more prosperous with the help of complex system theory and evolutionary game. Taking family enterprise as an example, this paper makes an attempt on exploring complex network modeling to study data processing method and abstraction method of complex network of TMT conflict. And the paper will consider the attribute and relational mapping of top management team as nodes and edges in complex network to discuss the direct correspondence between complex network structure and management team characteristics. Besides that, according to the multiple attribute decision making, the method to dig into core members of the top management team will be created on the basis of the degree, closeness, cluster coefficient, and betweenness. And then the article will devote to studying the impact of attributes to the inner mechanism of TMT conflict and team cohesion through the network characteristic analysis.
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    Trend Change Study of Climate Variables in Xin’anjiang-Fuchunjiang Watershed, China (2015)
    Hindawi
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    Publication Date: 2015-08-14
    Description: This study emphasizes the precipitation and the maximum and minimum temperature trend and presents the results of study in temporal and spatial scales, after performing statistical analysis of the Xin’anjiang-Fuchunjiang watershed. Statistical Mann Kendall and Theil Sen techniques were used to determine the trend and its magnitude, respectively, and for determining the start and abrupt change in the trend, Sequential Mann Kendall test has been performed. Furthermore, statistical tests were performed to determine the overall trend in the area at a regional basis. For the removal of the serial effect of the data, prewhitening technique is applied. In this study, statistical tests were performed at 1901–2013 precipitation and temperature series and then after detection of the change year precipitation data were divided into two different scenarios of 1901–1960 period and 1961–2013 period. The results showed that precipitation trend is insignificant while maximum and minimum temperature have increased during 1901–2013 period except for some stations of autumn and summer seasons.
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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing publication information (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Print ISSN: 1939-1404
    Topics: Geosciences
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    SAR Target Recognition via Joint Sparse Representation of Monogenic Signal (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: In this paper, the classification via sprepresentation and multitask learning is presented for target recognition in SAR image. To capture the characteristics of SAR image, a multidimensional generalization of the analytic signal, namely the monogenic signal, is employed. The original signal can be then orthogonally decomposed into three components: 1) local amplitude; 2) local phase; and 3) local orientation. Since the components represent the different kinds of information, it is beneficial by jointly considering them in a unifying framework. However, these components are infeasible to be directly utilized due to the high dimension and redundancy. To solve the problem, an intuitive idea is to define an augmented feature vector by concatenating the components. This strategy usually produces some information loss. To cover the shortage, this paper considers three components into different learning tasks, in which some common information can be shared. Specifically, the component-specific feature descriptor for each monogenic component is produced first. Inspired by the recent success of multitask learning, the resulting features are then fed into a joint sparse representation model to exploit the intercorrelation among multiple tasks. The inference is reached in terms of the total reconstruction error accumulated from all tasks. The novelty of this paper includes 1) the development of three component-specific feature descriptors; 2) the introduction of multitask learning into sparse representation model; 3) the numerical implementation of proposed method; and 4) extensive comparative experimental studies on MSTAR SAR dataset, including target recognition under standard operating conditions, as well as extended operating conditions, and the capability of outliers rejection.
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    Improved SAR Amplitude Image Offset Measurements for Deriving Three-Dimensional Coseismic Displacements (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: Offsets of synthetic aperture radar (SAR) images have played an important role in deriving complete three-dimensional (3-D) surface displacement fields in geoscientific applications. However, offset maps often suffer from multiple outliers and patch-like artifacts, because the standard offset-measurement method is a regular moving-window operation that does not consider the scattering characteristics of the ground. Here, we show that by focusing the offset measurements on predetected strong reflectors, the reliability and accuracy of SAR offsets can be significantly improved. Application to the 2011 Van (Turkey) earthquake reveals a clear deformation signal from an otherwise decorrelated interferogram, making derivation of the 3-D coseismic displacement field possible. Our proposed method can improve mapping of coseismic deformation and other ground displacements, such as glacier flow and landslide movement when strong reflectors exist.
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    Accelerated Probabilistic Learning Concept for Mining Heterogeneous Earth Observation Images (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: We present an accelerated probabilistic learning concept and its prototype implementation for mining heterogeneous Earth observation images, e.g., multispectral images, synthetic aperture radar (SAR) images, image time series, or geographical information systems (GIS) maps. The system prototype combines, at pixel level, the unsupervised clustering results of different features, extracted from heterogeneous satellite images and geographical information resources, with user-defined semantic annotations in order to calculate the posterior probabilities that allow the final probabilistic searches. The system is able to learn different semantic labels based on a newly developed Bayesian networks algorithm and allows different probabilistic retrieval methods of all semantically related images with only a few user interactions. The new algorithm reduces the computational cost, overperforming existing conventional systems, under certain conditions, by several orders of magnitude. The achieved speed-up allows the introduction of new feature models improving the learning capabilities of knowledge-driven image information mining systems and opening them to Big Data environments.
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    Assessing the Spectral Sensitivity of Martian Terrains to Iron Oxide Variations Using the SPLITS Model (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: The mineralogy and environmental history of Mars are being extensively studied through remote sensing observations paired with laboratory and in situ experiments. A significant portion of these experiments is being devoted to the identification and mapping of different iron oxides present in the Martian terrains. Among these compounds, goethite has been an object of great interest since its occurrence can be interpreted as mineralogical evidence of past aqueous activity on those landscapes. Although such experiments can provide valuable information regarding the presence of these minerals, the scope of the resulting observations may be hindered by logistics and cost-related constraints. We believe that predictive computer simulations can be employed to mitigate some of these constraints and contribute to the generation and validation of hypotheses in this area. Accordingly, we propose the use of SPLITS ( Sp ectral Li ght T ransport Model for S and) in investigations involving the spectral signatures of iron-bearing regions of Mars. In this paper, we initially demonstrate the predictive capabilities of the SPLITS model in this context through qualitative comparisons of modeled results with actual observations and measured data. Using the resulting modeled reflectance curves as our baseline data, we then perform a series of controlled computational experiments to investigate how variations on goethite and hematite content affect the spectral responses of Martian sand-textured soils.
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    Batch Methods for Resolution Enhancement of TIR Image Sequences (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: Thermal infrared (TIR) time series are exploited by many methods based on Earth observation (EO), for such applications as agriculture, forest management, and meteorology. However, due to physical limitations, data acquired by a single sensor are often unsatisfactory in terms of spatial or temporal resolution. This issue can be tackled by using remotely sensed data acquired by multiple sensors with complementary features. When nonreal-time functioning or at least near real-time functioning is admitted, the measurements can be profitably fed to a sequential Bayesian algorithm, which allows to account for the correlation embedded in the successive acquisitions. In this work, we focus on applications that allow the batch processing of the whole data sequences acquired in a fixed time interval. In this case, multiple options for improving the final product are offered by the Bayesian framework, based on both sequential and smoothing techniques. We consider several such Bayesian strategies and comparatively assess their performances in practical applications and through real thermal data acquired by the SEVIRI and MODIS sensors, encompassing the presence of multiple disturbance source, e.g., the cloud coverage of the illuminated scene.
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    An Algorithm for Retrieval of Surface Albedo From Small View-Angle Airborne Observations Through the Use of BRDF Archetypes as Prior Knowledge (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: Land surface albedo, qualifying the ratio of the radiant flux reflected from the land surface to the incident flux, is a key forcing parameter controlling the Earth’s energy budget. Previously, several BRDF archetypes were distilled from high-quality MODIS BRDF/Albedo products. In this study, we propose a method that largely relies on matching observed multiangular reflectances with the most appropriate of these prior BRDF archetypes to determine the amplitude and shape of the actual surface BRDFs, when directional signatures are insufficient. This method is first evaluated using an assortment of multisource BRDF data sets to demonstrate its viability for surface albedo estimates, and then is applied to airborne wide-angle infrared dual-mode line/area array scanner (WIDAS) from the Watershed Allied Telemetry Experimental Research (WATER) campaign in the Heihe River Basin of China in 2008. This algorithm makes use of the linear MODIS BRDF model to determine the BRDF archetypes needed as prior knowledge for intrinsic spectral albedo estimates. The intrinsic spectral albedos are then used to estimate actual spectral albedos by considering the proportion of direct and diffuse solar radiation. A spectral-to-broadband conversion is performed to generate the broadband albedo at shortwave regimes through the use of conversion coefficients derived from extensive radiative transfer simulations. A further validation confirms that the estimated albedos are consistent with in situ field measured albedos over available corn crop sites. This method provides a major advantage on utilizing generalized BRDF information derived from MODIS in conjunction with other instrument data that are acquired with less angular variation.
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    The Feasibility of Ocean Surface Current Measurement Using Pencil-Beam Rotating Scatterometer (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: Ocean current is highly related to the interaction between ocean and atmosphere. By measuring the speed and direction of the ocean current from space, we can investigate the ocean–atmosphere interaction on a global scale. The ocean–atmosphere interaction helps to maintain the balance that is essential for planet habitability. However, the conventional scatterometer is unable to measure the ocean current vector. To achieve this, a potentially feasible approach is to use a bigger antenna, a higher PRF, and measure the interferometric phase of two successive echoes. This paper derives four decorrelation factors, and provides the phase error model first. Then, an end-to-end simulation model is established, and it is used to analyze the feasibility of ocean surface current measurement from space. Based on the simulation model, the system parameters are optimized. The simulation results show that the current speed standard deviation (Std), which means the measurement accuracy, in along-track and cross-track direction is smaller than 0.1 m/s when the wind speed is larger than 4 m/s. The swath can be used for current vector inversion that is greater than 70% when the wind speed is larger than 7 m/s. Meanwhile, ${{bf K}_{{bf pc}}}$ of the modified scatterometer is computed and the results show that ${{bf K}_{{bf pc}}}$ is better than the traditional pencil-beam rotating scatterometer when the wind speed is larger than 6 m/s.
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    High-Resolution Three-Dimensional Imaging of Space Targets in Micromotion (2015)
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    Publication Date: 2015-08-14
    Description: High-resolution three-dimensional (3-D) radar imaging of space targets in micromotion plays a significant role in space target recognition and space situation awareness; thus, it has attracted extensive attention in recent years. Because of the fast rotation, some scattering centers are occluded by others, i.e., the scattering centers cannot be continuously illuminated by radar in the imaging interval, and their radar echoes are discontinuous. In this paper, a nonparametric 3-D imaging method based on scattering center trajectory association is proposed. It deals with target occlusion using the Riemannian manifold optimization and obtains focused imaging of targets in complex micromotion. The effectiveness of the proposed method is validated using simulated data.
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    Geolocation of Fast-Moving Objects From Satellite-Based Angle-of-Arrival Measurements (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: Recently, satellite-based systems have been introduced that utilize angle-of-arrival (AOA) measurements to geo-locate objects of interest. In the previous work, we considered the application of nonlinear optimization to AoA-based geolocation to these systems. This previous work, however, assumed that all noise sources were independent. In the case of fast-moving objects, however, there is a significant source of error due to the propagation time inherent in satellite-based observation of objects due to the difference between the location of the object when it is observed by a satellite, and the location of the object when it emitted the signal that is being measured. This introduces a systematic error into the system that cannot be resolved by the system proposed by Burchett et al. In this paper, we extend our prior work to account for the time-delay inherent in satellite-based geolocation systems, making this system accurate for fast-movers as well as fixed or slow-moving objects. Results demonstrating significant improvement in geolocation performance both in terms of accuracy and estimated error bounds are presented.
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    Improving Pixel-Based Change Detection Accuracy Using an Object-Based Approach in Multitemporal SAR Flood Images (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: Most of existing change detection methods could be classified into three groups, the traditional pixel-based change detection (PBCD), the object-based change detection (OBCD), and the hybrid change detection (HCD). Nevertheless, both PBCD and OBCD have disadvantages, and classical HCD methods belong to intuitive decision-level fusion schemes of PBCD and OBCD. There is no optimum HCD method as of yet. Analyzing the complementarities of PBCD and OBCD method, we propose a new unsupervised algorithm-level fusion scheme (UAFS-HCD) in this paper to improve the accuracy of PBCD using spatial context information through: 1) getting the preliminary change mask with PBCD at first to estimate some parameters for OBCD; 2) deriving the unchanged area mask to eliminate the areas without changes, reducing error amplification phenomenon of OBCD; and 3) obtaining the final change mask by means of OBCD method. Taking flood detection with multitemporal SAR data as an example, we compared the new scheme with some classical methods, including PBCD, OBCD, and HCD method and supervised manual trial-and-error procedure (MTEP). The experimental results of flood detection showed that the new scheme was efficient and robust, and its accuracy sometimes can even exceed MTEP.
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    Expand your professional network with IEEE (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
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    Scaling up to National/Regional Urban Extent Mapping Using Landsat Data (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: This paper describes a methodology to extract a consistent human settlement extent layer using Landsat data and its implementation in the Google Earth Engine platform. The approach allows the extraction of human settlement extents by means of the existing Landsat 5 and 7 data sets, allowing to check their evolution at 30-m spatial resolution. Since human settlements are the main proxy to people geographical distribution and to building locations, this layer may serve as a mean to disaggregate people/building counts at the regional/national level. The approach is tested in several parts of the world against existing ground truth data at the same spatial resolution in Brazil and China, as well as against extents manually extracted from VHR data in three different geographical areas: 1) Brazil; 2) South East China; and 3) Indonesia.
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    Investigating the Use of Low Frequencies in Urban Areas: Application to NLOS Configurations (2015)
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    Publication Date: 2015-08-14
    Description: Remote sensing of urban areas is mainly conducted at high frequencies, to obtain highly resolved images for classification, target detection, or urban areas monitoring for instance. We propose on the contrary to investigate the use of VHF-band for observation, between 120 and 360 MHz. In particular, our concern is to allow target detection, when the object is not in the line of sight (LOS) of the radar, which is typically an issue in urban areas. The benefits of low frequencies are highligted using measurements and simulations over a simple metallic model of two buildings surrounding a street.
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    Robust Observer-Based Feedback Control for Lipschitz Singularly Perturbed Systems (2015)
    Hindawi
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    Publication Date: 2015-08-14
    Description: The observer-based feedback control for singularly perturbed systems (SPSs) with Lipschitz constraint is addressed. A sufficient condition, independent of the perturbation parameter, for a full-order observer is presented in terms of linear matrix inequality (LMI) such that observation error is exponentially stable for all sufficiently small perturbation parameters. Then, for observer-based feedback control, a proper controller is constructed to guarantee the input-to-state stability of the system with regard to the observation error. Considering the convergence of observation error, the stability of the system can be obtained based on the input-to-state stability property. It is shown that the proposed method is simple and easy to operate. Moreover, the upper bound of the small perturbation parameter for stability of systems can be explicitly estimated with a workable computation way. Finally, two numerical examples show the effectiveness of the proposed method.
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    Direction of Arrival Estimation Based on DDOA and Self-Organizing Map (2015)
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    Publication Date: 2015-08-14
    Description: An effective two-level self-organizing map (SOM) neural network for direction of arrival (DOA) of sound signals estimation is proposed. The approach is based on the distance difference of arrival (DDOA) and a uniform linear sensor array in a 2D plane; it performs a nonlinear mapping between the DDOA vectors and angles of arrival (AOA). We found that the topological order of DDOA vectors and AOAs of same signals is uniform; thus, the topological order preserving of SOM network makes it valid to estimate AOA through DDOA. From the results of simulations and lake experiments, it is shown that the network has the advantage of accuracy and robustness, can be trained in advance, and is easy to implement.
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    A Max-Term Counting Based Knowledge Inconsistency Checking Strategy and Inconsistency Measure Calculation of Fuzzy Knowledge Based Systems (2015)
    Hindawi
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    Publication Date: 2015-08-14
    Description: The task of finding all the minimal inconsistent subsets plays a vital role in many theoretical works especially in large knowledge bases and it has been proved to be a NP-complete problem. In this work, at first we propose a max-term counting based knowledge inconsistency checking strategy. And, then, we put forward an algorithm for finding all minimal inconsistent subsets, in which we establish a Boolean lattice to organize the subsets of the given knowledge base and use leaf pruning to optimize the algorithm efficiency. Comparative experiments and analysis also show the algorithm’s improvement over past approaches. Finally, we give an application for inconsistency measure calculation of fuzzy knowledge based systems.
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    On Galileo’s Tallest Column (2015)
    Hindawi
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    Publication Date: 2015-08-13
    Description: The height at which an unloaded column will fail under its own weight was calculated for first time by Galileo for cylindrical columns. Galileo questioned himself if there exists a shape function for the cross section of the column with which it can attain a greater height than the cylindrical column. The problem is not solved since then, although the definition of the so named “constant maximum strength” solids seems to give an affirmative answer to Galileo’s question, in the form of shapes which seem to attain infinite height, even when loaded with a useful load at the top. The main contribution of this work is to show that Galileo’s problem is (i) an important problem for structural design theory of buildings and other structures, (ii) not solved by the time being in any sense, and (iii) an interesting problem for mathematicians involved in related but very different problems (as Euler’s tallest column). A contemporary formulation of the problem is included as a result of a research on the subject.
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    Multivariate Network Exploration and Presentations (2015)
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    Publication Date: 2015-08-15
    Description: This installment of Computer's series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on Visualization and Computer Graphics. The Web extra at http://youtu.be/E1PVTitj7h0 is a video demonstration of a novel solution to multivariate data visualization that helps users interactively explore data by combining standard presentations, from detailed views to high-level overviews.
    Print ISSN: 0018-9162
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    In-Memory Data Rearrangement for Irregular, Data-Intensive Computing (2015)
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    Publication Date: 2015-08-15
    Description: The data rearrangement engine (DRE) performs in-memory data restructuring to accelerate irregular, data-intensive applications. An emulation on a field-programmable gate array shows how the DRE could improve speedup, memory bandwidth, and energy consumption on three representative benchmarks.
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    Richard E. Merwin Scholarship House Advertisement (2015)
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    Publication Date: 2015-08-15
    Description: Advertisement, IEEE.
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    Research on Solving Systems of Nonlinear Equations Based on Improved PSO (2015)
    Hindawi
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    Publication Date: 2015-08-17
    Description: Solving systems of nonlinear equations is perhaps one of the most difficult problems in all of numerical computations, especially in a diverse range of engineering applications. The convergence and performance characteristics can be highly sensitive to the initial guess of the solution for most numerical methods such as Newton’s method. However, it is very difficult to select reasonable initial guess of the solution for most systems of nonlinear equations. Besides, the computational efficiency is not high enough. Aiming at these problems, an improved particle swarm optimization algorithm (imPSO) is proposed, which can overcome the problem of selecting reasonable initial guess of the solution and improve the computational efficiency. The convergence and performance characteristics of this method are demonstrated through some standard systems. The results show that the improved PSO for solving systems of nonlinear equations has reliable convergence probability, high convergence rate, and solution precision and is a successful approach in solving systems of nonlinear equations.
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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Information for Authors (2015)
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    Publication Date: 2015-08-04
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    Sensitivity of Reflectance to Water Vapor and Aerosol Optical Thickness (2015)
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    Publication Date: 2015-08-04
    Description: The atmospheric condition parameters used in the radiative transfer-based atmospheric correction (AC) are often uncertain. This uncertainty propagates to the estimated reflectance. The reflectance, is, however, not equally sensitive to all the parameters. A sensitivity analysis (SA) helps in prioritizing the parameters. The objective of this study was to perform an SA of reflectance to water vapor concentration ( $wv$ ) and aerosol optical thickness ( $AOT$ ). SA was performed using the Fourier amplitude sensitivity test (FAST) method, which computes sensitivity indices ( $text{SI}$ ) of these parameters. Besides variation in the two parameters, we also studied the effect of surface albedo on the $text{SI}$ by quantifying $text{SI}$ for three target surfaces (in the spectral range $text{0.44{-}0.96};upmu$ m): 1) a dark target (water); 2) a bright target (bare soil); and 3) a target having low albedo in the visible and high albedo in near-infrared range (forest). For $AOT$ , high ( $approx!text{0.9}$ ) $text{SI}$ values were observed at the nonwater absorption wavelengths. For $wv$ , high $text{SI}$ values were observed at wavelengths, where strong absorption features are loca- ed and when the surface albedo was high. For the dark target, the effect of $AOT$ was prominent throughout the spectral range. We found that the sensitivity of reflectance to $wv$ and $AOT$ is a function of wavelength, strength of the absorption features, and surface albedo. We conclude that $AOT$ is a more important parameter for dark targets than $wv$ even at the principal absorption feature. For bright targets, the importance of $wv$ and $AOT$ depends on the strength of the absorption feature.
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    Effect of Blue Jets on Atmospheric Composition: Feasibility of Measurement From a Stratospheric Balloon (2015)
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    Publication Date: 2015-08-04
    Description: The feasibility study of the HALESIS (High-Altitude Luminous Events Studied by Infrared Spectro-imagery) project is presented. The purpose of this experiment is to measure the atmospheric perturbation in the minutes following the occurrence of transient luminous events (TLEs) from a stratospheric balloon in the altitude range of 20–40 km. The instrumentation will include a spectro-imager embedded in a pointing gondola. Infrared signatures of a single blue jet were simulated under the assumption of local thermodynamic equilibrium (LTE), and were then compared with a panel of commercially available instrument specifications. The sensitivity of the signatures with a local perturbation of the main vibrational energy level populations of ${mathbf{rm CO}_{2}}$ , CO, NO, ${mathbf{rm O}_{3}}$ , and ${mathbf{rm H}_{2}}{rm O}$ was measured and the infrared signatures of a single blue jet taking into account non-LTE hypotheses were compared with the same panel of commercially available instrument specifications. Lastly, the feasibility of the study is discussed.
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    Gradient and Laplacian-Based Hyperspectral Anisotropic Diffusion (2015)
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    Publication Date: 2015-08-04
    Description: To improve accuracy and efficiency of object detection and classification with hyperspectral imagery (HSI), we propose a novel smoothing algorithm by coupling of a Laplacian-based reaction term to a classical divergence-based anisotropic diffusion partial differential equation (PDE). In addition, an adaptive parameter is introduced to regularize this nonlinear reaction-diffusion PDE by explicitly integrating the interband correlations with the noise level of each band in HSI. It is also well-known that the interband correlations can be implicitly embedded into the diffusion coefficient of the divergence-based PDE, to allow a selective smoothing that reduces the local homogeneous area variability while preventing smoothing across edges. Therefore, the interband correlations in HSI are exploited in the proposed method in both explicit and implicit ways. As a result, our algorithm is more effective at controlling the behavior of the diffusion evolution when compared to previous multi/hyperspectral diffusion algorithms. The simulations based on both synthetic data and real hyperspectral remote sensing data show that our algorithm can improve the hyperspectral data quality in terms of both visual inspection and image quality indices.
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    Cross-Domain Matching with Squared-Loss Mutual Information (2015)
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    Publication Date: 2015-08-04
    Description: The goal of cross-domain matching (CDM) is to find correspondences between two sets of objects in different domains in an unsupervised way. CDM has various interesting applications, including photo album summarization where photos are automatically aligned into a designed frame expressed in the Cartesian coordinate system, and temporal alignment which aligns sequences such as videos that are potentially expressed using different features. In this paper, we propose an information-theoretic CDM framework based on squared-loss mutual information (SMI). The proposed approach can directly handle non-linearly related objects/sequences with different dimensions, with the ability that hyper-parameters can be objectively optimized by cross-validation. We apply the proposed method to several real-world problems including image matching, unpaired voice conversion, photo album summarization, cross-feature video and cross-domain video-to-mocap alignment, and Kinect -based action recognition, and experimentally demonstrate that the proposed method is a promising alternative to state-of-the-art CDM methods.
    Print ISSN: 0162-8828
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    Semi-Continuity of Skeletons in Two-Manifold and Discrete Voronoi Approximation (2015)
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    Publication Date: 2015-08-04
    Description: The skeleton of a 2D shape is an important geometric structure in pattern analysis and computer vision. In this paper we study the skeleton of a 2D shape in a two-manifold $mathcal {M}$ , based on a geodesic metric. We present a formal definition of the skeleton $S(Omega )$ for a shape $Omega$ in $mathcal {M}$ and show several properties that make $S(Omega )$ distinct from its Euclidean counterpart in $mathbb {R}^2$ . We further prove that for a shape sequence $lbrace Omega _irbrace$ that converge to a shape $Omega$ in $mathcal {M}$ , the mapping $Omega righta- row overline{S}(Omega )$ is lower semi-continuous. A direct application of this result is that we can use a set $P$ of sample points to approximate the boundary of a 2D shape $Omega$ in $mathcal {M}$ , and the Voronoi diagram of $P$ inside $Omega subset mathcal {M}$ gives a good approximation to the skeleton $S(Omega )$ . Examples of skeleton computation in topography and brain morphometry are illustrated.
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    Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting (2015)
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    Publication Date: 2015-08-04
    Description: A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position of each point. We show that this leads to fast and accurate shape model matching when applied in the Constrained Local Model framework. We evaluate the technique in detail, and compare it with a range of commonly used alternatives across application areas: the annotation of the joints of the hands in radiographs and the detection of feature points in facial images. We show that our approach outperforms alternative techniques, achieving what we believe to be the most accurate results yet published for hand joint annotation and state-of-the-art performance for facial feature point detection.
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    Recognising Planes in a Single Image (2015)
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    Publication Date: 2015-08-04
    Description: We present a novel method to recognise planar structures in a single image and estimate their 3D orientation. This is done by exploiting the relationship between image appearance and 3D structure, using machine learning methods with supervised training data. As such, the method does not require specific features or use geometric cues, such as vanishing points. We employ general feature representations based on spatiograms of gradients and colour, coupled with relevance vector machines for classification and regression. We first show that using hand-labelled training data, we are able to classify pre-segmented regions as being planar or not, and estimate their 3D orientation. We then incorporate the method into a segmentation algorithm to detect multiple planar structures from a previously unseen image.
    Print ISSN: 0162-8828
    Electronic ISSN: 1939-3539
    Topics: Computer Science
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    IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
    Print ISSN: 0162-8828
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    Sparse Multi-View Consistency for Object Segmentation (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
    Description: Multiple view segmentation consists in segmenting objects simultaneously in several views. A key issue in that respect and compared to monocular settings is to ensure propagation of segmentation information between views while minimizing complexity and computational cost. In this work, we first investigate the idea that examining measurements at the projections of a sparse set of 3D points is sufficient to achieve this goal. The proposed algorithm softly assigns each of these 3D samples to the scene background if it projects on the background region in at least one view, or to the foreground if it projects on foreground region in all views. Second, we show how other modalities such as depth may be seamlessly integrated in the model and benefit the segmentation. The paper exposes a detailed set of experiments used to validate the algorithm, showing results comparable with the state of art, with reduced computational complexity. We also discuss the use of different modalities for specific situations, such as dealing with a low number of viewpoints or a scene with color ambiguities between foreground and background.
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    Asymptotic Behavior of Weak Solutions to the Generalized Nonlinear Partial Differential Equation Model (2015)
    Hindawi
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    Publication Date: 2015-07-30
    Description: This paper investigates the asymptotic behavior of weak solutions to the generalized nonlinear partial differential equation model. It is proved that every perturbed weak solution of the perturbed generalized nonlinear partial differential equations asymptotically converges to the solution of the original system under the large perturbation.
    Print ISSN: 1024-123X
    Electronic ISSN: 1563-5147
    Topics: Mathematics , Technology
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    Normal Forms of Hopf Bifurcation for a Reaction-Diffusion System Subject to Neumann Boundary Condition (2015)
    Hindawi
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    Publication Date: 2015-08-04
    Description: A reaction-diffusion system coupled by two equations subject to homogeneous Neumann boundary condition on one-dimensional spatial domain with is considered. According to the normal form method and the center manifold theorem for reaction-diffusion equations, the explicit formulas determining the properties of Hopf bifurcation of spatially homogeneous and nonhomogeneous periodic solutions of system near the constant steady state are obtained.
    Print ISSN: 1110-757X
    Electronic ISSN: 1687-0042
    Topics: Mathematics
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    Determining Replenishment Lot Size and Shipment Policy for an EPQ Inventory Model with Delivery and Rework (2015)
    Hindawi
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    Publication Date: 2015-08-03
    Description: The determination of production-shipment policies for a vendor-buyer system is dealt within this paper. The main objective is to derive the optimal replenishment lot size and shipment policy for an EPQ inventory model with multiple deliveries and rework. This inventory model contains two decision variables: the replenishment lot size and the number of deliveries. Previous researches solve this inventory model considering both variables to be continuous. However, the number of deliveries must be considered as a discrete variable. In this direction, this paper solves the inventory model considering two cases: Case 1: the replenishment lot size as a continuous variable and the number of shipments as a discrete variable and Case 2: the replenishment lot size and the number of shipments as discrete variables. The final results are two simple and easy-to-apply solution procedures to find the optimal values for the replenishment lot size and the number of deliveries for each case.
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    A Study of SUOWA Operators in Two Dimensions (2015)
    Hindawi
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    Publication Date: 2015-08-03
    Description: SUOWA operators are a new class of aggregation functions that simultaneously generalize weighted means and OWA operators. They are Choquet integral-based operators with respect to normalized capacities; therefore, they possess some interesting properties such as continuity, monotonicity, idempotency, compensativeness, and homogeneity of degree 1. In this paper, we focus on two dimensions and show that any Choquet integral with respect to a normalized capacity can be expressed as a SUOWA operator.
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    A New Biobjective Model to Optimize Integrated Redundancy Allocation and Reliability-Centered Maintenance Problems in a System Using Metaheuristics (2015)
    Hindawi
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    Publication Date: 2015-08-03
    Description: A novel integrated model is proposed to optimize the redundancy allocation problem (RAP) and the reliability-centered maintenance (RCM) simultaneously. A system of both repairable and nonrepairable components has been considered. In this system, electronic components are nonrepairable while mechanical components are mostly repairable. For nonrepairable components, a redundancy allocation problem is dealt with to determine optimal redundancy strategy and number of redundant components to be implemented in each subsystem. In addition, a maintenance scheduling problem is considered for repairable components in order to identify the best maintenance policy and optimize system reliability. Both active and cold standby redundancy strategies have been taken into account for electronic components. Also, net present value of the secondary cost including operational and maintenance costs has been calculated. The problem is formulated as a biobjective mathematical programming model aiming to reach a tradeoff between system reliability and cost. Three metaheuristic algorithms are employed to solve the proposed model: Nondominated Sorting Genetic Algorithm (NSGA-II), Multiobjective Particle Swarm Optimization (MOPSO), and Multiobjective Firefly Algorithm (MOFA). Several test problems are solved using the mentioned algorithms to test efficiency and effectiveness of the solution approaches and obtained results are analyzed.
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    Deterministic and Probabilistic Investigation on Multiple Crack Interactions in a Semi-Infinite Domain (2015)
    Hindawi
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    Publication Date: 2015-08-04
    Description: The investigation of multiple crack interactions in fracture mechanics is important to predict the safety and reliability of structures. This study aims to investigate the interactions of multiple parallel cracks in a semi-infinite domain in both deterministic and probabilistic ways by using an automated finite element modeling procedure and the Monte Carlo simulation. The stress intensity factor is considered as an indicator of failure and accurately evaluated by using the domain integral technique. The variation of the stress intensity factor according to the position, the length, and the number of cracks is demonstrated. In a probabilistic investigation, the effects of the number of cracks, the random distribution of the crack lengths, and the crack interactions to the failure probability are studied for a semi-infinite domain. The stress redistribution among multiple cracks, the effect of unevenly distributed crack lengths, and the combined effect of crack length uncertainties and a crack shielding effect have been examined.
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    Effects Comparison of Different Resilience Enhancing Strategies for Municipal Water Distribution Network: A Multidimensional Approach (2015)
    Hindawi
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    Publication Date: 2015-08-04
    Description: Water distribution network (WDN) is critical to the city service, economic rehabilitation, public health, and safety. Reconstructing the WDN to improve its resilience in seismic disaster is an important and ongoing issue. Although a considerable body of research has examined the effects of different reconstruction strategies on seismic resistance, it is still hard for decision-makers to choose optimal resilience enhancing strategy. Taking the pipeline ductile retrofitting and network meshed expansion as demonstration, we proposed a feasible framework to contrast the resilience enhancing effects of two reconstruction strategies—units retrofitting strategy and network optimization strategy—in technical and organizational dimension. We also developed a new performance response function (PRF) which is based on network equilibrium theory to conduct the effects comparison in integrated technical and organizational dimension. Through the case study of municipal WDN in Lianyungang, China, the comparison results were thoroughly shown and the holistic decision-making support was provided.
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    Timetable Design for Urban Rail Line with Capacity Constraints (2015)
    Hindawi
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    Publication Date: 2015-08-04
    Description: To design an efficient and economical timetable for a heavily congested urban rail corridor, a scheduling model is proposed in this paper. The objective of the proposed model is to find the departure time of trains at the start terminal to minimize the system cost, which includes passenger waiting cost and operating cost. To evaluate the performance of the timetable, a simulation model is developed to simulate the detailed movements of passengers and trains with strict constraints of station and train capacities. It assumes that passengers who arrive early will have more chances to access a station and board a train. The accessing and boarding processes of passengers are all based on a first-come-first-serve basis. When a station is full, passengers unable to access must wait outside until the number of waiting passengers at platform falls below a given value. When a train is full, passengers unable to board must wait at the platform for the next train to arrive. Then, based on the simulation results, a two-stage genetic algorithm is introduced to find the best timetable. Finally, a numerical example is given to demonstrate the effectiveness of the proposed model and solution method.
    Print ISSN: 1026-0226
    Electronic ISSN: 1607-887X
    Topics: Mathematics
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    Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction (2015)
    Hindawi
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    Publication Date: 2015-08-04
    Description: With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzy c-means) based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.
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    A Pest Management Model with Stage Structure and Impulsive State Feedback Control (2015)
    Hindawi
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    Publication Date: 2015-08-04
    Description: A pest management model with stage structure and impulsive state feedback control is investigated. We get the sufficient condition for the existence of the order-1 periodic solution by differential equation geometry theory and successor function. Further, we obtain a new judgement method for the stability of the order-1 periodic solution of the semicontinuous systems by referencing the stability analysis for limit cycles of continuous systems, which is different from the previous method of analog of Poincarè criterion. Finally, we analyze numerically the theoretical results obtained.
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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing publication information (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
    Print ISSN: 1939-1404
    Topics: Geosciences
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    A Spectral–Spatial Approach for Hyperspectral Image Classification Using Spatial Regularization on Supervised Score Image (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
    Description: This paper proposes a novel approach to classify hyperspectral (HS) images using both spectral and spatial information. It first consists of a supervised spectral dimension reduction step that transforms the HS image into a score image that has fewer channels. These channels are chosen so as to enhance distances between classes to be discriminated and to reduce background variability, thus leading to edges that correspond to actual class borders. In the second step, applying an edge-preserving spatial regularization on this score image leads to a lowered background variability. Therefore, in the third step, the pixel-wise classification of the regularized score image is greatly improved. We implement this approach using the partial least squares (PLS) method for spectral dimension reduction and the anisotropic diffusion for spatial regularization. We then compare linear discriminant analysis (LDA), K-nearest neighbors (KNN), and support vector machine (SVM) for the class decision. The effectiveness of our method was evaluated with three remotely sensed HS images. Its robustness was also assessed for different training sets, since the latter has a crucial influence on classification performance. On average, our method gave better results in terms of classification accuracy and was more robust than other classification methods tested with the same images.
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    Extreme Learning Machine With Composite Kernels for Hyperspectral Image Classification (2015)
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    Publication Date: 2015-08-04
    Description: Due to its simple, fast, and good generalization ability, extreme learning machine (ELM) has recently drawn increasing attention in the pattern recognition and machine learning fields. To investigate the performance of ELM on the hyperspectral images (HSIs), this paper proposes two spatial–spectral composite kernel (CK) ELM classification methods. In the proposed CK framework, the single spatial or spectral kernel consists of activation–function-based kernel and general Gaussian kernel, respectively. The proposed methods inherit the advantages of ELM and have an analytic solution to directly implement the multiclass classification. Experimental results on three benchmark hyperspectral datasets demonstrate that the proposed ELM with CK methods outperform the general ELM, SVM, and SVM with CK methods.
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    Hyperspectral Image Classification Using Spectral–Spatial Composite Kernels Discriminant Analysis (2015)
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    Publication Date: 2015-08-04
    Description: This paper proposes a framework for hyperspectral images (HSIs) classification with composite kernels discriminant analysis (CKDA). The CKDA uses the spectral and spatial information extracted by Gaussian weighted local mean operator (GWLM) and is suitable to solve few labeled samples classification problem of HSI, which has very important practical significance for the case that training samples are insufficient due to high cost. Experimental results show that the spatial information extracted by GWLM can greatly improve the performance, and demonstrate the superiority of CKDA for HSI classification in the case of few labeled samples. Compared with other state-of-the-art spectral–spatial kernel methods, the proposed methods also show very good advantages, especially the parallel kernel method.
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    Dynamic Ensemble Selection Approach for Hyperspectral Image Classification With Joint Spectral and Spatial Information (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
    Description: Accurate generation of a land cover map using hyperspectral data is an important application of remote sensing. Multiple classifier system (MCS) is an effective tool for hyperspectral image classification. However, most of the research in MCS addressed the problem of classifier combination, while the potential of selecting classifiers dynamically is least explored for hyperspectral image classification. The goal of this paper is to assess the potential of dynamic classifier selection/dynamic ensemble selection (DCS/DES) for classification of hyperspectral images, which consists in selecting the best (subset of) optimal classifier(s) relative to each input pixel by exploiting the local information content of the image pixel. In order to have an accurate as well as computationally fast DCS/DES, we proposed a new DCS/DES framework based on extreme learning machine (ELM) regression and a new spectral–spatial classification model, which incorporates the spatial contextual information by using the Markov random field (MRF) with the proposed DES method. The proposed classification framework can be considered as a unified model to exploit the full spectral and spatial information. Classification experiments carried out on two different airborne hyperspectral images demonstrate that the proposed method yields a significant increase in the accuracy when compared to the state-of-the-art approaches.
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    Comparing the Effect of Preprocessing Transformations on Methods of Land-Use Classification Derived From Spectral Soil Measurements (2015)
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    Publication Date: 2015-08-04
    Description: Advanced classifiers, e.g., partial least squares discriminant analysis (PLS-DA) and random forests (RF), have been recently used to model reflectance spectral data in general, and of soil properties in particular, since their spectra are multivariate and highly collinear. Preprocessing transformations (PPTs) can improve the classification accuracy by increasing the variability between classes while decreasing the variability within classes. Such PPTs are common practice prior to a PLS-DA, but are rarely used for RF. The objectives of this paper are twofold: to compare the performances of PLS-DA and RF for modeling the spectral reflectance of soil in changed land-uses with different treatments and to compare the effects of nine different PPTs on the prediction accuracy of each of these classification methods. Differences in six physical, biological, and chemical soil properties of changed land-uses from the northern Negev Desert in Israel were evaluated. Significant differences were found between soil properties, which are used to classify land-uses and treatments. Depending on the dataset, different PPTs improved the classification accuracy by 11%–24% and 32%–42% for PLS-DA and RF, respectively, in comparison to the spectra without PPT. Out of the PPTs tested, the generalized least squares weighting (GLSW)-based transformations were found to be the most effective for most classifications using both PLS-DA and RF. Our results show that both PLS-DA and RF are suitable classifiers for spectral data, provided that an appropriate PPT is applied.
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    Kernel Multivariate Spectral–Spatial Analysis of Hyperspectral Data (2015)
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    Publication Date: 2015-08-04
    Description: This paper contributes the concept of spectral–spatial kernel-based multivariate analysis (KMVSSA) based on the statistical principle of multivariate statistics. The essence of proposed framework is to expose the inherent structure and meaning revealed within spectral and spatial features through various statistical methods in hyperspectral remotely sensed data. This kernel-based framework is investigated to incorporate the spectral and spatial information simultaneously for dimension reduction and classification of hyperdimensional datasets. The method uses multivariate analysis to choose and apply a transform matrix that the transformed components are as orthogonal as possible. This nonlinear framework is derived by means of the theory of complete orthonormal systems. KMVSSA exhibits great flexibility by the combination of spectral and spatial features. We investigate the possibility of using KMVSSA for the classification of hyperspectral images and dimension reduction. The proposed framework is examined and compared in different merits with several hyperspectral images in different conditions (urban/agricultural area and size of the training set). Experimental results show that the proposed framework can meaningfully enhance the dimensionality reduction and also it greatly improves the overall as well as per class classification accuracies. We demonstrate a comprehensive comparison of some state of the art hyperspectral image classification methods.
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    Knowledge-Encoded Granular Neural Networks for Hyperspectral Remote Sensing Image Classification (2015)
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    Publication Date: 2015-08-04
    Description: Development of intelligent decision-making systems for complex problems, such as land covers classification of hyperspectral remote sensing (HSRS) images, requires efficient interpretation of available information through conceptual rather than numerical level. Granular neural network (GNN) in combination with the granular representation of information using linguistic terms is one such system. GNN takes the fuzzified input information and processes them with neural network (NN) architecture, where the network structure is transparent enough to interpret the processing steps. Further, knowledge encoding has been considered as one of the principal elements of intelligent decision-making systems. This paper proposes a new model of knowledge-encoded GNNs for land cover classification of HSRS images. Knowledge encoding is done using neighborhood rough sets (NRSs) that explore the local/contextual information. The encoded knowledge using NRS is obtained in the form of dependency rules with respect to the output class labels of land covers and these rules determine appropriate number of hidden nodes of GNNs. The dependency factors obtained during rule generation are used for initializing the connecting weights of GNNs. NRS is also used here in the selection of a subset of features for reducing the burden of high-dimensional fuzzy-granulated feature space of HSRS image. The proposed model thus explores jointly the advantages of fuzzy granulation, GNNs, and feature selection and knowledge encoding using NRS. Superiority of the model to similar other methods are justified in land covers classification of two HSRS images acquired by different remotely placed sensors.
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    Nonlinear Unmixing by Using Different Metrics in a Linear Unmixing Chain (2015)
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    Publication Date: 2015-08-04
    Description: Several popular endmember extraction and unmixing algorithms are based on the geometrical interpretation of the linear mixing model, and assume the presence of pure pixels in the data. These endmembers can be identified by maximizing a simplex volume, or finding maximal distances in subsequent subspace projections, while unmixing can be considered a simplex projection problem. Since many of these algorithms can be written in terms of distance geometry, where mutual distances are the properties of interest instead of Euclidean coordinates, one can design an unmixing chain where other distance metrics are used. Many preprocessing steps such as (nonlinear) dimensionality reduction or data whitening, and several nonlinear unmixing models such as the Hapke and bilinear models, can be considered as transformations to a different data space, with a corresponding metric. In this paper, we show how one can use different metrics in geometry-based endmember extraction and unmixing algorithms, and demonstrate the results for some well-known metrics, such as the Mahalanobis distance, the Hapke model for intimate mixing, the polynomial post-nonlinear model, and graph-geodesic distances. This offers a flexible processing chain, where many models and preprocessing steps can be transparently incorporated through the use of the proper distance function.
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    Single-Species Detection With Airborne Imaging Spectroscopy Data: A Comparison of Support Vector Techniques (2015)
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    Publication Date: 2015-08-04
    Description: Progress in mapping plant species remotely with imaging spectroscopy data is limited by the traditional classification framework, which carries the requirement of exhaustively defining all classes (species) encountered in a landscape. As the research objective may be to map only one or a few species of interest, we need to explore alternative classification methods that may be used to more efficiently detect a single species. We compared the performance of three support vector machine (SVM) methods designed for single-class detection—binary (one-against-all) SVM, one-class SVM, and biased SVM—in detecting five focal tree and shrub species using data collected by the Carnegie Airborne Observatory over an African savanna. Prior to this comparison, we investigated the effects of training data amount and balance on binary SVM and evaluated alternative methods for tuning one-class and biased SVMs. A key finding was that biased SVM was generally best parameterized by crown-level cross validation paired with the tuning criterion proposed by Lee and Liu [1] . Among the different single-class methods, binary SVM showed the best overall performance (average F-scores 0.43–0.78 among species), whereas one-class SVM showed very poor performance (F-scores 0.09–0.46). However, biased SVM produced results similar to those obtained with binary SVM (F-scores 0.40–0.72), despite using labeled training data from only the focal class. Our results indicate that both binary and biased SVMs can work well for remote single-species detection, while both methods, particularly biased SVM, greatly reduce the amount of training data required compared with traditional multispecies classification.
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    Spectral–Spatial Hyperspectral Image Classification Using $ell_{1/2}$ Regularized Low-Rank Representation and Sparse Representation-Based Graph Cuts (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
    Description: Hundreds of narrow contiguous spectral bands collected by a hyperspectral sensor have provided the opportunity to identify the various materials present on the surface. Moreover, spatial information, enforcing the assumption that the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, two predominant approaches have been developed to exploit the spatial information. First, by decomposing each pixel and the spatial neighborhood into a low-rank form, the spatial information can be efficiently integrated into the spectral signatures. Meanwhile, in order to describe the low-rank structure of the decomposed data more precisely, an $ell_{1/2}$ norm regularization is introduced and a discrete algorithm is proposed to solve the combined optimization problem by the augmented Lagrange multiplier (ALM) and a half-threshold operator. Second, a graph cuts segmentation algorithm has been applied on the sparse-representation-based probability estimates of the hyperspectral data to further improve the spatial homogeneity of the material distribution. Experimental results on four real hyperspectral data with different spectral and spatial resolutions have demonstrated the effectiveness and versatility of the proposed spatial information-fused approaches for hyperspectral image classification.
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    Hyperspectral Image Classification With Limited Labeled Training Samples Using Enhanced Ensemble Learning and Conditional Random Fields (2015)
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    Publication Date: 2015-08-04
    Description: Classification of hyperspectral imagery using few labeled samples is a challenging problem, considering the high dimensionality of hyperspectral imagery. Classifiers trained on limited samples with abundant spectral bands tend to overfit, leading to weak generalization capability. To address this problem, we have developed an enhanced ensemble method called multiclass boosted rotation forest (MBRF), which combines the rotation forest algorithm and a multiclass AdaBoost algorithm. The benefit of this combination can be explained by bias-variance analysis, especially in the situation of inadequate training samples and high dimensionality. Furthermore, MBRF innately produces posterior probabilities inherited from AdaBoost, which are served as the unary potentials of the conditional random field (CRF) model to incorporate spatial context information. Experimental results show that the classification accuracy by MBRF as well as its integration with CRF consistently outperforms the other referenced state-of-the-art classification methods when limited labeled samples are available for training.
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    Combination of Clustering and Ranking Techniques for Unsupervised Band Selection of Hyperspectral Images (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
    Description: Curse of dimensionality is a major disadvantage for classification of hyperspectral imagery since a large number of bands need to be dealt with. Band selection is a task to reduce the number of bands. An unsupervised band selection method is proposed in this article. It is a three-step procedure. In the first step, characteristics (attributes) of the bands are found out. Next, redundancy among the bands is removed by executing clustering operation. At last, the remaining bands, which are nonredundant among themselves, are ranked according to their discriminating capability. Discriminating capability is calculated by measuring the capacitory discrimination of the bands. Results are compared with four state-of-the-art methods: a band elimination method, a ranking-based, and two clustering-based band selection methods to demonstrate the effectiveness of the proposed method. Four evaluation measures, namely: 1) classification accuracy; 2)  Kappa coefficient; 3) class separability, and 4) entropy, are calculated over the selected bands to assess the efficiency of the selected bands. The proposed method shows promising results compared to them.
    Print ISSN: 1939-1404
    Topics: Geosciences
    Published by Institute of Electrical and Electronics Engineers (IEEE)
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    Fast Forward Feature Selection of Hyperspectral Images for Classification With Gaussian Mixture Models (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-04
    Description: A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation (k-CV). In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM can be updated when the estimation of the classification rate is computed, rather than re-estimate the full model. Secondly, using marginalization of the GMM, submodels can be directly obtained from the full model learned with all the spectral features. Experimental results for two real hyperspectral data sets show that the method performs very well in terms of classification accuracy and processing time. Furthermore, the extracted model contains very few spectral channels.
    Print ISSN: 1939-1404
    Topics: Geosciences
    Published by Institute of Electrical and Electronics Engineers (IEEE)
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    Semisupervised Hyperspectral Band Selection Via Spectral–Spatial Hypergraph Model (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2015-08-04
    Description: Band selection is an essential step toward effective and efficient hyperspectral image classification. Traditional supervised band selection methods are often hindered by the problem of lacking enough training samples. To address this problem, we propose a semisupervised band selection method that allows contribution from both labeled and unlabeled hyperspectral pixels. This method first builds a hypergraph model from all hyperspectral samples to measure the similarity among pixels. We show that hypergraph can capture relationship among pixels in both spectral and spatial domain. In the second step, a semisupervised learning method is introduced to propagate class labels to unlabeled samples. Then a linear regression model with group sparsity constraint is used for band selection. Finally, hyperspectral pixels with selected bands are used to train a support vector machine (SVM) classifier. The proposed method is tested on three benchmark datasets. Experimental results demonstrate its advantages over several other band selection methods.
    Print ISSN: 1939-1404
    Topics: Geosciences
    Published by Institute of Electrical and Electronics Engineers (IEEE)
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