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  • Articles  (1,467)
  • Institute of Electrical and Electronics Engineers (IEEE)  (1,467)
  • Public Library of Science (PLoS)
  • 2015-2019  (1,467)
  • 1945-1949
  • IEEE Geoscience and Remote Sensing Letters  (1,467)
  • 40722
  • 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)
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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)
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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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    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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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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    An Improved Phase Correlation Method Based on 2-D Plane Fitting and the Maximum Kernel Density Estimator (2015)
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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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    Improvement of Forest Carbon Estimation by Integration of Regression Modeling and Spectral Unmixing of Landsat Data (2015)
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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)
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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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    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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    Multicontextual Mutual Information Data for SAR Image Change Detection (2015)
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    Publication Date: 2015-08-11
    Description: How to produce the difference data of the two temporal images is a crucial factor in image change detection. In this letter, we propose multicontextual mutual information data (MMID) based on the bivariate Gaussian distribution (BGD) for synthetic aperture radar (SAR) image change detection and illustrate their superiorities over the classical difference data. MMID, which are an improved form of image spatial mutual information, are constructed based on the quadrilateral Markov random field (QMRF) and can be factored into the linear combination of the entropies. Then to adapt MMID to the change detection, we construct the 2-D entropies based on the BGD. In this way, MMID are able to capture the intertemporal statistical dependence of the two temporal images and thus can be taken as the feature-level difference data rather than the pixel-level data. The maximum-likelihood method, the automatic threshold method, and the Markov random field method are performed on the MMID of the real two temporal SAR images for the change detection. Experimental results demonstrate the superiorities of MMID over the traditional difference data.
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    HyperMix: An Open-Source Tool for Fast Spectral Unmixing on Graphics Processing Units (2015)
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    Publication Date: 2015-08-11
    Description: Spectral unmixing has been a popular technique for analyzing remotely sensed hyperspectral images. The goal of unmixing is to find a collection of pure spectral constituents (called endmembers ) that can explain each (possibly mixed) pixel of the scene as a combination of endmembers, weighted by their coverage fractions in the pixel or abundances . Over the last years, many algorithms have been presented to address the three main parts of the spectral unmixing chain: 1) estimation of the number of endmembers; 2) identification of the endmember signatures; and 3) estimation of the per-pixel fractional abundances. However, to date, there is no standardized tool that integrates these algorithms in a unified framework. In this letter, we present HyperMix, an open-source tool for spectral unmixing that integrates different approaches for spectral unmixing and allows building unmixing chains in graphical fashion, so that the end-user can define one or several spectral unmixing chains in fully configurable mode. HyperMix provides efficient implementations of most of the algorithms used for spectral unmixing, so that the tool automatically recognizes if the computer has a graphics processing unit (GPU) available and optimizes the execution of these algorithms in the GPU. This allows for the execution of spectral unmixing chains on large hyperspectral scenes in computationally efficient fashion. The tool is available online from http://hypercomphypermix.blogspot.com.es and has been validated with real hyperspectral scenes, providing state-of-the-art unmixing results.
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    Adaptive Fission Particle Filter for Seismic Random Noise Attenuation (2015)
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    Publication Date: 2015-08-11
    Description: Seismic signals are nonlinear, and the seismic state-space model can be described as a nonlinear system. The particle filter (PF) method, as an effective method for estimating the state of a nonlinear system, can be applied to deal with seismic random noise attenuation. However, PF suffers from sample impoverishment caused by resampling, which results in serious loss of valid seismic information and leads to inaccurate representation of the reflected signal. To address the impoverishment issue and to further improve the particle quality, we propose a novel method to suppress seismic random noise—the adaptive fission particle filter (AFPF). In AFPF, all the particles undergo a fission process and produce “offspring” particles to maintain particle diversity. To implement the adaptation and to monitor the degree of fission, we apply a fission factor, which takes into account weights that indicate the quality of the particles. This leads to significant improvements in the particle quality, i.e., the proportion of highly weighted particles is increased. The effective seismic information provided by the resulting particles reproduces the true signal more reliably, reducing the bias of PF. In addition, we establish a dynamic state-space model suitable for seismic signals. Experimental results on synthetic records and field data illustrate the superior performance of AFPF in noise attenuation and reflected signal preservation compared with the PF.
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    Global Mosaics of the Relative Height Error From TanDEM-X Quicklooks (2015)
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    Publication Date: 2015-08-11
    Description: The primary objective of the TanDEM-X mission is the generation of a global high-precision digital elevation model (DEM) by using synthetic aperture radar interferometry. This letter presents the developed strategy for estimating the relative height error of the TanDEM-X DEM on a global scale. The mosaicking process of the final DEM combines all acquisitions at full resolution and is expected to be finished by late 2016. On the other hand, global mosaics can be generated starting from quicklook images already available for each single input data take. These downsized mosaics are operationally used to analyze the performance improvement that can be achieved by combining multiple acquisitions over the same ground areas and are a powerful mean for optimizing further acquisition planning. This letter reports the expected global performance of the final TanDEM-X product in advance of the full-resolution DEM. Knowledge of the global status of the TanDEM-X DEM relative height error is fundamental for optimizing the acquisition strategy and, therefore, the final performance and represents a valuable input for the scientific community as well as for selecting suitable areas for further interferometric experiments on a global scale.
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    A Support Vector Machine-Based Particle Filter Method for Improved Flooding Classification (2015)
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    Publication Date: 2015-08-11
    Description: Support vector machines (SVMs) have been applied to land cover classification, and a number of studies have demonstrated their ability to increase classification accuracy. The high correlation between the data set and SVM training model parameters indicates the high performance of the classification model. To improve the correlation, research has focused on the integration of SVMs and other algorithms for data set selection and SVM training model parameter estimation. This letter proposes a novel method, based on a particle filter (PF), of estimating SVM training model parameters according to an observation system. By treating the SVM training function as the observation system of the PF, the new method automatically updates the SVM training model parameters to values that are more appropriate for the data set and can provide a better classification model than can the original model, wherein the parameters are set by trial and error. Various experiments were conducted using Radarsat-2 synthetic aperture radar data from the 2011 Thailand flood. The proposed method provides superior performance and a more accurate analysis compared with the standard SVM.
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    Height Extraction of Maize Using Airborne Full-Waveform LIDAR Data and a Deconvolution Algorithm (2015)
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    Publication Date: 2015-08-11
    Description: Maize is a widely planted crop in China and in other areas of the world and plays an important role in grain production. Monitoring the growth status of maize using remote sensing technology is an important component of precision agriculture and height, as a crucial growth indicator for maize, can be retrieved from light detection and ranging (LIDAR) data. However, height extraction for crops, such as maize using airborne laser scanning point clouds results in a great number of uncertainties and challenges. Here, airborne full-waveform LIDAR data were used to extract maize height. In the first step, a workflow was designed based on the Gold deconvolution algorithm combined with a basic data process technique. The method was then tested and was determined to be effective for capturing the portion of the waveform interacting with the tops of vegetation, characterized by lower amplitude stemming from the ground. Therefore, the number of second returns from point clouds was dramatically increased. During the experiment, the number of point clouds increased nearly 50% for three of the four maize plots, as compared with the original point clouds. Compared with the commonly used Gaussian fitting algorithm, the deconvolution algorithm had the advantage of extracting an accurate position for overlapping weak signals. The height percentiles indicated that the original and Gaussian decomposition derived point clouds data underestimated and deconvolution algorithm can accurately reflect the true height of maize, particularly for the 75% and 95% height percentiles.
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    Burned-Area Mapping at the Subpixel Scale With MODIS Images (2015)
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    Publication Date: 2015-08-11
    Description: The Moderate Resolution Imaging Spectroradiometer (MODIS) is an important data set in global burned-area mapping. The MODIS global burned-area product has a coarse spatial resolution at approximately 500 m, which often introduces errors to the mapped burned areas. In this letter, a novel subpixel mapping (SPM) approach was proposed to produce burned-area maps at the fine spatial resolution similar to Landsat imagery, by exploring the spectral and spatial information provided by the second and fifth bands of MODIS. The proposed SPM approach aims to refine the estimate of burned areas, which have been detected by the MODIS global burned-area product. The performance of the proposed SPM approach was assessed with an experiment area containing six burned areas, by comparing with the MODIS burned-area product MCD45. The result shows that the average omission error decreased from 52.26% for MCD45 to 16.74% for SPM, and the average commission error decreased from 21.76% for MCD45 to 12.54% for SPM. The kappa value increased from 0.5583 for MCD45 to 0.8756 for SPM, indicating that the proposed SPM approach is effective in reducing the influence of the coarse spatial resolution of MODIS imagery in mapping a burned area and refining existing global burned-area products.
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    Local Parameter Estimation of Topographic Normalization for Forest Type Classification (2015)
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    Publication Date: 2015-08-11
    Description: Radiometric distortions caused by rugged terrain make the classification of forest types from satellite imagery a challenge. Various band-specific topographic normalization models are expected to eliminate or reduce these effects. The quality of these models also depends on the approach to estimate empirical parameters. Generally, a global estimation of these parameters from a whole satellite image is simple, but it may tend to overcorrection, particularly for larger areas. A land-cover-specific method usually performs better, but it requires obtaining a priori land classification, which presents another challenge in many cases. Empirical parameters can be directly estimated from local pixels in a given window. In this letter, we propose and evaluate a central-pixel-based parameter estimation method for topographic normalization using local window pixels. We tested the method with Landsat 8 imagery and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) in very rough terrain with diverse forest types. Visual comparison and statistical analyses showed that the proposed method performed better at a range of window sizes compared with an uncorrected image or with a global parameter estimation approach. The intraclass spectral variability of each forest type has been reduced significantly, and it can yield higher accuracy of forest type classification. The proposed method does not require the a priori knowledge of land covers. Its simplicity and robustness suggest that this method has the potential to be a standard preprocessing approach for optical satellite imagery, particularly for rough terrain.
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    Unsupervised Detection of Earthquake-Triggered Roof-Holes From UAV Images Using Joint Color and Shape Features (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-11
    Description: Many methods have been developed to detect damaged buildings due to earthquake. However, little attention has been paid to analyze slightly affected buildings. In this letter, an unsupervised method is presented to detect earthquake-triggered “ roof-holes ” on rural houses from unmanned aerial vehicle (UAV) images. First, both orthomosaic and gradient images are generated from a set of UAV images. Then, a modified Chinese restaurant franchise model is used to learn an unsupervised model of the geo-object classes in the area by fusing both oversegmented orthomosaic and gradient images. Finally, “roof-holes” on rural houses are detected using the learned model. The performance of the proposed method is evaluated in terms of both qualitative and quantitative indexes.
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    Recursive Automatic Target Generation Process in Subpixel Detection (2015)
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    Publication Date: 2015-08-11
    Description: Automatic target generation process (ATGP) has been used in a wide range of applications in hyperspectral image analysis. It performs a sequence of orthogonal subspace projections to extract potential targets of interest. This letter presents a recursive version of the ATGP, which is referred to as the recursive ATGP (RATGP) and has three advantages over the ATGP as follows: 1) there is no need of inverting a matrix as the ATGP does for finding each new target; 2) there is a significant reduction in the computational complexity in the hardware design due to its recursive structure; and 3) there is an automatic stopping rule that can be derived by the Neyman–Pearson detection theory to terminate the algorithm.
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    Automatic Road Extraction From Remote Sensing Images Based on a Normalized Second Derivative Map (2015)
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    Publication Date: 2015-08-11
    Description: In this letter, we propose a novel automatic algorithm for road extraction from remote sensing images. The algorithm includes low- and high-level processing. In the low-level processing, we determine a normalized second derivative map of road profiles of a generalized bar shape, which is width invariant and contrast proportional, and accordingly obtain initial road center pixels. In the high-level processing, using the map and initial center pixels, we initially determine road segments. The segments are then locally refined using their orientation randomness and length-to-width ratio and further refined via global graph-cut optimization. A final road network is thereby extracted in a robust manner. Experimental results demonstrate that the proposed algorithm provides noticeably more robust and higher road extraction performance in various images compared with the existing algorithms.
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    Cell-Based Two-Step Scalar Deadzone Quantization for High Bit-Depth Hyperspectral Image Coding (2015)
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    Publication Date: 2015-08-11
    Description: Remote sensing images often need to be coded and/or transmitted with constrained computational resources. Among other features, such images commonly have high spatial, spectral, and bit-depth resolution, which may render difficult their handling. This letter introduces an embedded quantization scheme based on two-step scalar deadzone quantization (2SDQ) that enhances the quality of transmitted images when coded with a constrained number of bits. The proposed scheme is devised for use in JPEG2000. It is named cell-based 2SDQ since it uses cells, i.e., small sets of wavelet coefficients within the codeblocks defined by JPEG2000. Cells permit a finer discrimination of coefficients in which to apply the proposed quantizer. Experimental results indicate that the proposed scheme is especially beneficial for high bit-depth hyperspectral images.
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    Combining Landsat TM/ETM+ and HJ-1 A/B CCD Sensors for Monitoring Coastal Water Quality in Hong Kong (2015)
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    Publication Date: 2015-08-11
    Description: High suspended solid (SS) concentrations in coastal waters are aesthetically undesirable, and adversely affect fisheries and coastal ecosystems. Environmental agencies usually require frequent measurements of SS over coastal regions at a spatially detailed level for water quality assessment and control. To develop a method for SS estimation in the complex coastal waters of Hong Kong, an archive of 57 Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and HJ-1 A/B Charged Couple Device (CCD) images over a 13-year period from January 2000 to December 2012 was used. Atmospherically corrected Landsat TM/ETM+ and HJ-1 A/B CCD bands 1–4 along with 240 in situ field samples of SS concentration collected within 2 h of image acquisition, were used to develop and validate regression models over a wide range of SS concentrations from 0.5–56.0 mg/L. The best representation of actual SS concentrations was given by the log-transformed combination of Band 2 (Green, 0.52–0.60 $mumbox{m}$ ) and Band 3 (Red, 0.63–0.69 $mumbox{m} $ ), with correlation coefficient (R) of 0.85, root-mean-square error of 2.60 mg/L and mean absolute error of 2.04 mg/L. This is attributed to the sensitivity of SS to green and red wavelengths specific to the characteristic refractive index and grain size of SS found in Hong Kong waters. This letter is considered more robust than previous studies, due to the much larger number of images and in situ samples used for model development and validation, as well as the different times of year and wide range of SS concentrations investigated.
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    Spectral Indices for Estimating Exposed Carbonate Rock Fraction in Karst Areas of Southwest China (2015)
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    Publication Date: 2015-08-11
    Description: The quantitative estimation of the fractional cover of carbonate rock (CR) is critical for natural resource management and ecological conservation in karst areas. Based on the analysis of spectral properties of CR together with other land cover types, we proposed two CR indices (CRIs) and established the model that represents the relationships between the CRIs and the fractional cover of CR. Then, the fractional cover of CR was estimated by using the developed model. Experimental results on Landsat-8 Operational Land Imager images acquired at Southwestern China demonstrated the effectiveness of the developed model. Compared with other indices, the proposed CRIs show the highest correlations with the fractional cover of CR.
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    IEEE Geoscience and Remote Sensing Letters institutional listings (2015)
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    Publication Date: 2015-11-24
    Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.
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    IEEE Geoscience and Remote Sensing Letters publication information (2015)
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    Publication Date: 2015-11-24
    Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.
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    Publication Date: 2015-08-28
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    Publication Date: 2015-08-28
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    Bayesian Inference for Inversion in Synthetic Aperture Imaging Radiometry (2016)
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    Publication Date: 2016-07-22
    Description: The inverse problem of synthetic aperture imaging radiometers (SAIRs) has been demonstrated to be not well posed. The regularization methods are crucial for providing unique and stable solutions in the reconstruction of radiometric brightness temperature (BT) maps. Different to deterministic ones, a new approach is presented by referring to the rule of Bayesian inference, providing a probability model of regularized constraints to combat the ill-posedness of finite-dimensional discrete inverse problems. In addition, the SAIR inverse problem can be converted into the probability estimation of the reconstructed BT. Furthermore, in application to both uniformly and nonuniformly spaced arrays, our method can obtain the optimal solution adaptively and avoid the dilemma of choosing the optimal regularization parameter. Finally, simulation results illustrating the effectiveness and performance of the proposed method are provided.
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    Range-Cell-Focusing Algorithm Combined With Enhanced MUSIC for Close Target Imaging (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: A range-cell-focusing algorithm is proposed in order to improve the quality of the target image. In a synthetic aperture radar (SAR) imaging system, the range resolution depends on the frequency bandwidth and determines the ability to distinguish between targets that are very close to each other. In cases where the resolution and the SNR from the environment are not adequate, targets cannot be accurately visualized. In order to successively classify targets that are close, we are combining an enhanced-multiple-signal-classification spectrum as a weighting function to reproduce the raw data. The proposed algorithm improves classification and separation for close targets while suppressing artifacts in the final images. The targets of interest are stationary point scatterers. The results are obtained from both simulated and experimental data to demonstrate that the proposed algorithm provides better performance than a conventional SAR imaging algorithm, the range migration algorithm.
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    Improved Multiscale Edge Detection Method for Polarimetric SAR Images (2016)
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    Publication Date: 2016-07-22
    Description: This letter presents a multiscale edge detection method for multilook polarimetric synthetic aperture radar (PolSAR) images based on the nonsubsampled contourlet transform (NSCT). The NSCT can provide flexible multiscale and directional decomposition. In the multiscale decomposition, the coefficients of the nonsubsampled pyramid in the NSCT are calculated via maximizing the polarimetric contrast between the adjacent subband levels, instead of using the difference of the adjacent subbands as used in the additive noise model. By this way, we make the NSCT applicable to PolSAR data and multiband data. Then, the edges are detected in the NSCT domain based on a fusion of the directional subband coefficients at different scales. Experimental results with both simulated and real PolSAR data show that the present approach is robust to noise and the extracted edges are complete and continuous.
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    On the Spatial Resolution of GNSS Reflectometry (2016)
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    Publication Date: 2016-07-22
    Description: A method for defining the spatial resolution of a Global Navigation Satellite System reflectometry delay–Doppler map (DDM) and of any derived geophysical product is proposed. An effective spatial resolution is derived as a function of measurement geometry and delay–Doppler (DD) interval, and as a more appropriate representation of resolution than the geometric resolution previously used in the literature. The definition more accurately accounts for variations in the scattered power across different pixels of the DDM and more accurately includes the power spreading effect caused by the Woodward ambiguity function. The dependence of the effective resolution on incidence angle, receiver altitude, and DD interval is analyzed and compared with the dependence of the geometric resolution with similar parameters.
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    Junction-Based Correspondence Estimation of Plant Point Cloud Data Using Subgraph Matching (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: Laser scanner-captured 3-D point cloud data analysis is becoming more commonly used for remote sensing and plant science applications. Because of nonrigidity and complexity, reconstructing a 3-D model of a plant is extremely challenging. Existing algorithms often fail to find correct correspondences for plantlike thin structures. We address the problem of finding 3-D junction points in plant point cloud data as a first step of this correspondence matching process. Temporarily, we transform the 3-D problem into 2-D by performing appropriate coordinate transformations to the neighborhood of each 3-D point. Our proposed method has two steps. First, a statistical dip test of multimodality is performed to detect the nonlinearity of the local 2D structure. Then, each branch is approximated by sequential random-sample-consensus line fitting and a Euclidean clustering technique. The straight line parameters of each branch are extracted using total-least-squares estimation. Finally, the straight line equations are solved to determine if they intersect in the local neighborhood. Such junction points are good candidates for subsequent correspondence algorithms. Using these detected junction points, we formulate a correspondence algorithm as a subgraph matching problem and show that, without using traditional descriptor similarity-based matching, good correspondences can be obtained by simply considering geodesic distances among graph nodes. Experiments on synthetic and real ( Arabidopsis plant) data show that the proposed method outperforms the state of the art.
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    Improved Envisat Altimetry Ice Sheet Elevation Change Data Processing Algorithms Using Repeat-Track Analysis (2016)
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    Publication Date: 2016-07-22
    Description: Repeat-track analysis is commonly utilized to generate elevation change time series from satellite radar altimetry over ice sheets. It requires surface gradient (SG) correction due primarily to orbital drifts and radar-related empirical corrections caused by radar scatters from ice surface and potential subsurface. In this letter, two approaches, namely, the use of a digital elevation model (DEM) and the modified repeat-track analysis, which uses the accumulated Envisat altimetry profiles, are applied to correct the SG over both Greenland ice sheet (GrIS) and Antarctic ice sheet (AIS). By comparing the root mean square (rms) of elevation change time series after SG correction, the percentage of data (rms< 1 m) obtained by using modified repeat-track analysis is found to be 85% and 88% for the GrIS and AIS, respectively, as opposed to 45% and 44% if the DEM method is used. Furthermore, three cases are studied to assess empirical corrections for elevation retrieved from both ice-1 and ice-2 algorithms over the AIS. We conclude that the modified repeat-track analysis is more effective to remove topographic induced error. For the ice-2 algorithm, waveform shape parameters are needed in addition to applying corrections from changes in backscatter coefficients. The trend of elevation changes from the ice-1 algorithm with only backscatter analysis agrees with that from the ice-2 algorithm with corrections from backscatter coefficient changes and waveform shape parameters. This study could provide a potential data processing recipe for generating improved satellite radar altimetry elevation time series over ice sheets.
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    Ship Rotated Bounding Box Space for Ship Extraction From High-Resolution Optical Satellite Images With Complex Backgrounds (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: Extracting ships from complex backgrounds is the bottleneck of ship detection in high-resolution optical satellite images. In this letter, we propose a nearly closed-form ship rotated bounding box space used for ship detection and design a method to generate a small number of highly potential candidates based on this space. We first analyze the possibility of accurately covering all ships by labeling rotated bounding boxes. Moreover, to reduce search space, we construct a nearly closed-form ship rotated bounding box space. Then, by scoring for each latent candidate in the space using a two-cascaded linear model followed by binary linear programming, we select a small number of highly potential candidates. Moreover, we also propose a fast version of our method. Experiments on our data set validate the effectiveness of our method and the efficiency of its fast version, which achieves a close detection rate in near real time.
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    Efficient Airport Detection Using Line Segment Detector and Fisher Vector Representation (2016)
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    Publication Date: 2016-07-22
    Description: In this letter, a two-stage method for airport detection on remote sensing images is proposed. In the first stage, a new algorithm composed of several line-based processing steps is used for extraction of candidate airport regions. In the second stage, the scale-invariant feature transformation and Fisher vector coding are used for efficient representation of the airport and nonairport regions and support vector machines employed for classification. In order to evaluate the performance of the proposed method, extensive experiments are conducted on airports around the world with different layouts. The measures used in the evaluation are accuracy, sensitivity, and specificity. The proposed method achieved an accuracy of 94.6%, which was benchmarked with two previous methods to prove its superiority.
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    Onboard Processing for Data Volume Reduction in High-Resolution Wide-Swath SAR (2016)
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    Publication Date: 2016-07-22
    Description: High-resolution wide-swath synthetic aperture radar (SAR) systems are very attractive for the observation of dynamic processes on the Earth's surface, but they require the downlink of a huge volume of data. In order to comply with azimuth ambiguity requirements, in fact, a pulse repetition frequency much higher than the required processed Doppler bandwidth is often desirable. The volume of downlinked data, however, can be drastically reduced by performing Doppler filtering and decimation on board. A finite-impulse-response filter with a relatively small number of taps suffices to suppress the additional ambiguous components and to recover the original impulse response. This strategy is of special relevance for staggered SAR systems, which are typically characterized by a high oversampling factor. The proposed data reduction technique is also baseline for Tandem-L, where onboard Doppler filtering, resampling, and decimation will be jointly implemented.
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    Uniaxial Effective Permittivity of Anisotropic Bicontinuous Random Media Using NMM3D (2016)
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    Publication Date: 2016-07-22
    Description: In this letter, we generate anisotropic bicontinuous media with different vertical and horizontal correlation functions. With the computer-generated bicontinuous medium, we then use numerical solutions of Maxwell equations in 3-dimensions (NMM3D) to calculate the anisotropic effective permittivities and the effective propagation constants of V and H polarizations. The copolarization phase difference (CPD) of VV and HH is then derived. The CPDs have recently been applied to the retrieval of snow water equivalent, snow depth, and anisotropy. The NMM3D simulation results are also compared with the results of the strong permittivity fluctuations in the low frequency limit and compared against the Maxwell–Garnett mixing formula.
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    IEEE Geoscience and Remote Sensing Letters Institutional Listings (2016)
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    Publication Date: 2016-07-22
    Description: The IEEE GRS Society is grateful for the support given by the organizations listed and invites applications for Institutional Listings from other firms interested in the field of geoscience and remote sensing.
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    IEEE Geoscience and Remote Sensing Letters information for authors (2016)
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    Publication Date: 2016-07-22
    Description: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
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    The Stability of UWB Low-Frequency SAR Images (2016)
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    Publication Date: 2016-07-22
    Description: This letter presents an analysis of prefiltered clutter ultrawideband (UWB) very high frequency synthetic aperture radar (SAR) images. The image data are reorganized into subvectors based on the observation of the image-pair magnitude samples. Based on this approach, we present a statistical description of the SAR clutter obtained by the subtraction between two real SAR images. The statistical analysis based on bivariate distribution data organized into different intervals of magnitude can be an important tool to further understand the properties of the backscattered signal for low-frequency SAR images. In this letter, it is found that, for “good” image pairs, the subtracted image has Gaussian distributed clutter backscattering and that the noise mainly consists of the thermal noise and, therefore, speckle noise does not have to be considered. This is a consequence of the stable backscattering for a UWB low-frequency SAR system.
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    ASCAT and QuikSCAT Azimuth Modulation of Backscatter Over East Antarctica (2016)
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    Publication Date: 2016-07-22
    Description: For most land and ice surfaces, the measured radar backscatter at the spatial resolution of a wind scatterometer is insensitive to the azimuth angle. However, for regions of East Antarctica, the backscatter strongly depends on the azimuth angle. This relationship between backscatter and azimuth angle is often modeled with a Fourier series. Although previous work has separately examined the data from QuikSCAT, a Ku-band scatterometer, and the Advanced Scatterometer (ASCAT), a C-band scatterometer, this letter compares the two on the same high-resolution grid. We find that, although QuikSCAT has superior azimuth angle coverage (due to its measurement geometry) compared to ASCAT, both are suitable to estimate the radar backscatter azimuth angle modulation of East Antarctica. The ASCAT data exhibit a much larger azimuth modulation than the QuikSCAT data. This is attributed to the different wavelengths of the microwave signal: At C-band (5.7 cm), East Antarctica has features that show radar backscatter to be more dependent on azimuth angle than at Ku-band (2.2 cm). This letter also examines the ASCAT and QuikSCAT azimuth modulation over previously identified regions of wind glaze. Although azimuth modulation is expected to be minimal over wind glaze, we find the wind-glaze regions to contain more structure than previously suggested.
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    Polarimetric Calibration Based on Lexicographic-Basis Decomposition (2016)
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    Publication Date: 2016-07-22
    Description: A novel polarimetric calibration scheme on the basis of lexicographic matrix decomposition is proposed, and a new polarimetric active radar calibrator (PARC) with two independently rotatable antennas is designed to obtain the lexicographic matrices. Thus, the proposed method is realizable and can operate with the lexicographic target vectors instead of the polarization scattering matrix (PSM). The base elements of the lexicographic target vectors are the vectors corresponding to the new PARC with the antennas at different orientations. Moreover, the elements of the vector are just the elements of the PSM. Hence, the error coefficients corresponding to a polarimetric measurement system are directly achieved, and the polarimetric calibration is simple and accurate. Experimental results confirm the superiority of the presented strategy. A 35-dB improvement in the system cross-polarization isolation is obtained.
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    A Target Detection Method Based on Low-Rank Regularized Least Squares Model for Hyperspectral Images (2016)
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    Publication Date: 2016-07-22
    Description: Target detection plays an important role in the field of hyperspectral image (HSI) remote sensing. In this letter, a novel matched subspace detector based on low-rank regularized least squares (LRLS-MSD) is proposed for hyperspectral target detection. As pixels in an HSI have global correlation and can be represented in subspace, the low-rank regularization is introduced in the least squares model. An effective algorithm is presented to solve the problem. Then, the detection results are generated according to the generalized likelihood ratio test with statistical hypotheses. The experimental results suggest an advantage of the low-rank regularization over other classical target detection methods.
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    An Improvement on SSA Method for EM Scattering From Electrically Large Rough Sea Surface (2016)
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    Publication Date: 2016-07-22
    Description: An improved facet model for the predication of the normalized radar cross section (NRCS) of electrically large rough sea surface was proposed based on the first-order small slope approximation (SSA-1) method, the Bragg scattering mechanism, and the specular scattering mechanism. The proposed method is able to evaluate both the complex reflective function and NRCS of electrically large sea surfaces from as low as ultrahigh frequency band to as high as Ka-band. The main idea is that a tilt sea facet can be regarded as the superposition of a planner facet and the microscopic profile; the latter is assumed to be a set of sinusoidal ripple patches. Thus, the integration kernel in SSA-1 over several small facets can be replaced by a large facet with a short wave modification. The efficiency increases because of the much larger mesh size than SSA. Then, both the backscattering and bistatic scattering NRCS results calculated by the proposed method were compared with those predicated by SSA-1, and all of the results show that the proposed method has the merits of high calculation efficiency as well as calculation accuracy.
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    Experimental Results of Passive SAR Imaging Using DVB-T Illuminators of Opportunity (2016)
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    Publication Date: 2016-07-22
    Description: In this letter, pioneering experimental results of passive synthetic aperture radar (SAR) imaging are presented. The classical active SAR radar operates in monostatic geometry. The SAR sensor presented in this letter is a passive radar utilizing commercial Digital Video Broadcasting—Terrestrial transmitters as illuminators of opportunity. It works in a bistatic configuration, where the receiver is placed on a moving platform and the transmitter is placed on the ground and is stationary. The imaged scenes are stationary surfaces on Earth such as agriculture or urban areas, buildings, etc. In this letter, pioneering results of signal processing verified by a measurement campaign are presented. In the experiment, two synchronized passive radar receivers were mounted on a small airborne platform. The main goal of the presented experiment was to verify the possibility of ground imaging using passive SAR technology and validate previously presented theoretical results.
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    IEEE Geoscience and Remote Sensing Letters Institutional Listings (2016)
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    Publication Date: 2016-06-24
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    Publication Date: 2016-06-24
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    IEEE Geoscience and Remote Sensing Letters information for authors (2016)
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    Publication Date: 2016-06-24
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    A Study of Land Surface Albedo Conversion Formulas Using Three-Dimensional Canopy Radiative Transfer Modeling (2016)
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    Publication Date: 2016-07-22
    Description: Land surface albedo defines the fraction of short-wave radiation reflected by the Earth's surface and controls the surface energy balance; thus, it is important for environmental and climate scientific communities. Remote sensing is the only means to globally map land surface albedo, however for it to be of use to the aforementioned communities, it must be accurate with respect to Global Climate Observing System (GCOS) requirements. Sources of error are introduced in each step of the provision of land surface albedo products, whereby this letter intends to investigate sources of error introduced by the narrow-band-to-broad-band conversion formula step. The radiative transfer modeling of vegetation is used to simulate spectral albedo over complex 3-D vegetation canopies; then narrow-band-to-broad-band conversion formulas for numerous sensors are applied on the spectral albedo to compute the broad-band albedo (BBA), and the accuracy of formulas is investigated. Results indicate that the effectiveness of conversion formulas is determined by the sensor, depending on the placement and number of the sensor wavebands, the ecosystem complexity, and the broad-band range of the BBA.
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    A Modified Locality-Preserving Projection Approach for Hyperspectral Image Classification (2016)
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    Publication Date: 2016-07-22
    Description: Locality-preserving projection (LPP) is a typical manifold-based dimensionality reduction (DR) method, which has been successfully applied to some pattern recognition tasks. However, LPP depends on an underlying adjacency graph, which has several problems when it is applied to hyperspectral image (HSI) processing. The adjacency graph is artificially created in advance, which may not be suitable for the following DR and classification. It is also difficult to determine an appropriate neighborhood size in graph construction. Additionally, only the information of local neighboring data points is considered in LPP, which is limited for improving classification accuracy. To address these problems, a modified version of the original LPP called MLPP is proposed for hyperspectral remote-sensing image classification. The idea is to select a different number of nearest neighbors for each data point adaptively and to focus on maximizing the distance between nonnearest neighboring points. This not only preserves the intrinsic geometric structure of the data but also increases the separability among ground objects with different spectral characteristics. Moreover, MLPP does not depend on any parameters or prior knowledge. Experiments on two real HSIs from different sensors demonstrate that MLPP is remarkably superior to other conventional DR methods in enhancing classification performance.
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    Three-Dimensional Electromagnetic Model-Based Pose Estimation Using Fully Polarimetric Wideband Radar (2016)
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    Publication Date: 2016-07-22
    Description: The pose of a rotational symmetry target in a wideband radar is the angle between its symmetry axis and the radar line of sight. It is a required parameter for target absolute attitude measurement using the bistatic radar measuring system. In this letter, a 3-D electromagnetic model (3-D EM model)-based method is proposed for estimating the target pose from fully polarimetric wideband radar measurements. The 3-D EM model is established offline based on the target geometric structure. Scattering features at different target poses can be accurately predicted by this model. In the proposed algorithm, a synthetic high-resolution range profile (HRRP), which is proved to be only related to the target pose, is first generated from the sum of echoes in radar copolarization channels. Then, by matching this synthetic HRRP with the synthetic HRRPs recovered by the 3-D EM model, the target pose is obtained. Experiments using both data predicted by a high-frequency electromagnetic code and data measured in an anechoic chamber verify the validity of the method.
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    IEEE Geoscience and Remote Sensing Letters publication information (2016)
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    Publication Date: 2016-07-22
    Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.
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    An Adaptive Filter for Aeromagnetic Compensation Based on Wavelet Multiresolution Analysis (2016)
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    Publication Date: 2016-07-22
    Description: In aeromagnetic compensation, the multicolinearity of the Tolles–Lawson model is one of the main factors limiting the performance of the coefficient-estimating methods. Aside from the intrinsic characteristic of the Tolles–Lawson model, data filtering as a preprocessing step in estimating the coefficients has a significant impact on the multicolinearity. To solve this problem, an adaptive filter based on wavelet multiresolution analysis is proposed. By modifying the wavelet decomposition results of the Mallat algorithms and reconstructing the signal, the adaptive filter can be used to establish a system of linear equations not only by capturing the frequency characteristics of the aircraft maneuvers as is usual but also by having a weakened multicolinearity. Simulation results illustrate the effectiveness of the proposed adaptive filter.
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    Simulated Annealing With Variogram-Based Optimization to Quantify Spatial Patterns of Trees Extracted From High-Resolution Images (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: The recognition of spatial patterns of trees from satellite images is important for forestry, horticulture, and wildlife management. As a spatial optimization problem, we used variogram matching as the objective function for simulated tree arrangements and their simulated reproductions. We developed a variogram difference-based spatial simulated annealing (VDBSSA) method and applied it in reproducing alternate simulated tree arrangements in several orchards in India. Regular, linear, and sparse configurations with clearly separated objects could be distinguished. QuickBird 2 panchromatic images (0.6-m resolution) were binarized, followed by generating simulated images of a similar appearance. The performance of VDBSSA for alternate arrangements with the same spatial structure was explained by pattern characteristics. The density of objects inversely influenced the number of possible alternate arrangements. A sparse configuration gave more choices of alternate spatial arrangements than a dense configuration. VDBSSA is valuable for generating a pattern of well identifiable objects from a remote sensing image.
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    An Improved Oblique Projection Method for Sea Clutter Suppression in Shipborne HFSWR (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: Sea clutter has a major impact on the detection performance of a shipborne high-frequency surface wave radar (HFSWR) system. Due to the platform motion of shipborne HFSWR, the Doppler spectrum of the first-order sea clutter suffers from some broadening so that the targets submerged in this broadening Doppler spectrum can be hardly detected. In this letter, an improved oblique projection (IOP) method, combining the oblique projection (OP) algorithm and the method of sea clutter suppression in the Doppler domain, is proposed to suppress sea clutter in both Doppler domain and spatial domain for shipborne HFSWR. Compared with the OP and the orthogonal weighting algorithms, the proposed IOP algorithm is shown to give far superior suppression results in the Doppler domain and can achieve better azimuth estimation results based on real data.
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    Attenuation of Millimeter-Wave in a Sand and Dust Storm (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: The attenuation of millimeter-wave signals in a sand and dust storm (SDS) can be used to retrieve SDS parameters. A parabolic wave equation method is proposed to simulate millimeter-wave propagation in an SDS, including the effects of absorption and Rayleigh scattering by dust particles with given height profiles of particle-size distribution and total number density. Three different aperture fields of the transmitting antenna are also chosen to simulate wave attenuation under different sets of SDS parameters.
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    Anisotropy Characteristics of Exposed Gravel Beds Revealed in High-Point-Density Airborne Laser Scanning Data (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: The aim of this study was to examine the relationship between the anisotropy direction of exposed gravel bed and flow direction. Previous studies have shown that the anisotropy direction of a gravel bed surface can be visually determined in the elliptical contours of 2-D variogram surface (2DVS). In this letter, airborne laser scanning (ALS) point clouds were acquired at a gravel bed, and the whole data set was divided into a series of 6 m $times$ 6 m subsets. To estimate the direction of anisotropy, we proposed an ellipse-fitting-based automatic procedure with consideration given to the grain size characteristic $d_{50}$ to estimate the primary axis of anisotropy [hereafter referred to as the primary continuity direction (PCD)] in the 2DVS. The ALS-derived PCDs were compared to the flow directions (for both high and low flow) derived from hydrodynamic model simulation. Comparison of ALS-derived PCDs and simulated flow directions suggested that ALS-derived PCDs could be used to infer flow direction at different flow rates. Furthermore, we found that the ALS-derived PCDs estimated from any elliptical contour of the 2DVS exhibited a similar orientation when the contours of the 2DVS reveal the clear anisotropic structure, demonstrating the robustness of the technique.
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    Attitude-Steering Strategy for Squint Spaceborne Synthetic Aperture Radar (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: For spaceborne synthetic aperture radar systems, minimizing the Doppler centroid variation over the range swath may bring many benefits, including improving the accuracy of the Doppler centroid estimation, alleviating the complexity of the data processing, and so on. In the current literature, it is well known that the Doppler centroid for the broadside-looking mode can be reduced to 0 Hz by employing 2-D attitude-steering law which includes a yaw and a pitch steering. However, for the squint mode, 2-D steering is not enough. In this letter, the iso-Doppler surface is first derived using the motion state vectors of the Earth and the satellite, based on which the 3-D attitude-steering strategy including an additional roll steering is proposed to minimize the Doppler centroid variation. Finally, the effectiveness of the proposed strategy is validated by the simulation results when the antenna pointing error is either absent or present.
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    Range-Dependent Map-Drift Algorithm for Focusing UAV SAR Imagery (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: Synthetic aperture radar (SAR) systems mounted on unmanned aerial vehicles (UAVs) are usually sensitive to trajectory deviations that cause serious motion error in the recorded data. In this letter, a novel range-dependent map-drift algorithm (RDMDA) is developed to accommodate the range-variant characteristics of severe motion errors. Utilizing the algorithm as a core estimate, we come up with a robust motion compensation strategy for the UAV SAR imagery. RDMDA outperforms the conventional MDA in both accuracy and robustness while it keeps similar efficiency. Real data experiment shows that the proposed approach is appropriate for precise imaging of UAV SAR systems equipped with only a low-accuracy inertial navigation system.
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    An Elaborately Designed Virtual Frame to Level Aeromagnetic Data (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: Aeromagnetic data are usually contaminated by line-to-line errors, which are most often visible as stripe patterns in a 2-D data map. Leveling is a critical step to eliminate these errors in data processing and interpretation. The conventional tie-line leveling technique involves control lines (tie lines) which are perpendicular to flight lines to extract the leveling errors. By contrast, there are also some other methods to level without the need for tie lines, which can save considerable survey cost. However, a main setback when leveling without tie lines is to distinguish the cross-line gradients from the leveling errors, making the leveled results inferior. In this letter, we revisit tie-line leveling as a nonlinear spatial-filtering technique and analyze the inadequacy of it, based on which a new approach is developed to extract the leveling errors without additional tie lines. The approach includes two steps: designing a virtual frame and leveling based on this frame. The virtual frame is elaborately designed to ensure that each extracted point can represent the local field-intensity level of a flight line and each virtual cross line can bypass the localized areas of magnetic anomalies; hence, some difficulties when leveling without tie lines are overcome. Afterward, two real field data examples are tested to demonstrate the effectiveness of this approach.
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    Side-Lobe Reduction for Radio Frequency Interference Suppression via Clipping of Strong Scatterers (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: In this letter, we present a normalized least mean square filter with clipping of strong scatterers to suppress radio frequency interference (RFI) in low-frequency synthetic aperture radar (SAR) systems. In this filter, the impulse responses of bright scatterers are clipped after range compression to improve the performance of narrow-band RFI estimation for adaptive line enhancer. Then, the estimated RFI is subtracted from the original echo for SAR imaging; thus, the range side-lobes of bright scatterers are reduced successfully. This method is particularly applicable to situations where there are bright scatterers in the image scene. The performance for RFI suppression is investigated by using the simulated data and the raw data as well.
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    Change Detection Using Global and Local Multifractal Description (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: In this letter, we apply the multifractal formalism to land cover change detection on very high spatial resolution data. Specifically, multifractal spectra are determined and, with modifications, are used as an initial general indicator of change on the subsets of IKONOS and Pleiades images. Next, we calculate Hölder exponents for each pixel in the images and use them to generate a change mask. Our analysis shows that Hölder exponents enable a detailed evaluation of changes in land cover. A comparison with change detection based solely on panchromatic images shows that the multifractal description method has significant advantages as it reduces the number of false positives. In addition, we show that our change detection results are comparable with other multiscale techniques.
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    Disaster Damage Assessment of Buildings Using Adaptive Self-Similarity Descriptor (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: Assessment of damage caused by a disaster is significant for coordinating emergency response teams and planning emergency aid. In this letter, a robust method for rapid building damage assessment is proposed using pre- and postevent EO images and building footprints. The method uses a local self-similarity descriptor (SSD) for change detection in buildings, which is shown to be robust against variations in global illumination and small local deformations. The use of building footprints helps reduce the false alarms due to changes in nonbuilding areas. Footprint is also used to differentiate small and large buildings, extract the boundary region of a building, and adapt the descriptor computation accordingly. It is shown that the adaptive SSD provides a more accurate measure of local damage on the building. The 2010 Haiti Earthquake and Typhoon Haiyan 2013 Philippines are analyzed with the proposed method, and 75/82% true positive rate and 25/15% false positive rate are obtained for detection of collapsed buildings with respect to the ground truth data of UNITAR/UNOSAT and HOT.
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    Results of Real-Time Kinematic Positioning Based on Real GPS L5 Data (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: The Global Positioning System (GPS) is currently being upgraded from a dual- to a triple-frequency constellation. Although a full triple-frequency constellation will not be realized before 2021, we can already make use of these new signals for positioning. This letter presents the initial results of real-time kinematic (RTK) ambiguity resolution and positioning based on real L5 measurements combined with L1 and L2C measurements. The L2C is the new second civil signal that is like L5, also transmitted by the GPS Block IIF satellites. The results, obtained for a zero baseline and short baselines between some MGEX receivers, demonstrate that the L5 data indeed improve the time-to-first-fix ambiguities, with 24%–49% with respect to dual $text{L}1+text{L}2text{C}$ , to, on average, less than 1 min based on $text{L}1+text{L}2text{C}+text{L}5$ with four to five satellites. It is also shown that single-frequency RTK is performing better when, instead of L1, the L5 frequency is used, provided that the baseline is not too long. Positioning accuracy, especially the float, is improved when going from dual- to triple-frequency measurements, despite the large position errors that occur at some times, caused by a poor geometry based on only four satellites. In the near future, with more Block IIF satellites launched, these geometry defects will occur less frequently.
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    Image Analysis for Altimetry Waveform Selection Over Heterogeneous Inland Waters (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: Over inland waters the altimetry radar return is typically contaminated by both land and surrounding vegetation. Moreover, the radar returns corresponding to floodplain vegetation and exposed mud flats can have similar waveform shapes to those reflected from open water surfaces so that the derived water surface elevations are either inaccurate or difficult to extract. This letter presents a robust and automated method based on image analysis of satellite imagery for the selection of altimetry waveforms over inundated zones. The altimetry footprint is assessed as being inundated if the radiometric response of an image kernel within the footprint conforms to known remotely sensed responses for water. The method was applied in the lower middle Fly floodplain of the Fly River in the Western Province of Papua New Guinea (PNG). Altimetry waveforms were assessed for inundation extent and vegetation cover, with those that met threshold levels being flagged for further retracking and water surface elevation monitoring. The results show that the method accurately identified> 90% of inundated sites along altimeter ground tracks and correctly selected waveforms reflected from water surfaces.
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    SNR Enhancement for Downhole Microseismic Data Using CSST (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: Noise contamination is a significant issue in microseismic data processing due to the low magnitude of high-frequency downhole microseismic signals induced during fluid injection. In this letter, a noncoherent noise attenuation technique based on cycle spinning shearlet transform (CSST) is presented. The CSST algorithm is implemented in three steps. In the first step, we forcibly shift signals so that their features change positions and orientations and then transform the noisy data into shearlet domain to obtain coefficients of different scales and directions. In the second stage, we apply hard thresholding to the resulting coefficients of individual component. Finally, we transform them back into the original domain and averagely superimpose the filtering results to preserve the amplitudes of the signals. The resulting methodology is tested on the synthetic and field datasets that were recorded with a vertical array of receivers. The experimental results show that the proposed CSST algorithm has better performance than the conventional threshold-based shearlet transform denoising method in terms of both high-frequency signal preservation and noise attenuation.
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    Wideband Dual-Polarized and Dual-Monopulse Compact Array for SAR System Integration Applications (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-22
    Description: A wideband dual-polarized and dual-monopulse array with highly self-compact configuration is proposed for synthetic aperture radar (SAR) applications. To investigate the monopulse performances, a monopulse comparator network integrated with a 16 $times$ 32 antenna array is designed to form a complete monopulse array. In order to realize the complicated monopulse array and avoid the disadvantages from conventional machining methods in manufacturing waveguide structures, a 3-D metal-direct-printing technique is adopted in this work to obtain better mechanical and electrical performances. Experimental results show that the monopulse array can operate in a wide bandwidth of 12.5% in the Ku-band. Port isolation and polarization isolation better than 30 and 45 dB are achieved, respectively. Furthermore, satisfactory monopulse performances in sum- and difference-patterns are also obtained in the wideband. In particular, a high efficiency of 95% is achieved at the center frequency of the sum-patterns, validating the design feasibility and merit of the 3-D printing technique in SAR applications.
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    Relative Ionospheric Ranging Delay in LEO GNSS Oceanic Reflections (2015)
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    Publication Date: 2015-04-01
    Description: Global Navigation Satellite System (GNSS) reflectometry leverages signals of opportunity to remotely sense the Earth's surface for a variety of science investigations. However, ionospheric refraction affects GNSS reflections detected at low Earth orbit (LEO). While multifrequency GNSS enables the elimination of most of the ionospheric error, single-frequency missions are still susceptible to this ranging delay. Motivated by the planned launch of Cyclone GNSS (CYGNSS) in 2016, a single-frequency reflectometry mission, this letter presents a simulation of the relative ionospheric delay that will shift the Delay-Doppler Map (DDM) data product. A mathematical model is presented that defines and characterizes signal propagation delay in the DDM. The model differentiates direct and reflected signals as a sum of path lengths, atmospheric refraction effects, and noise. We simulate representative ionospheric delays from the model associated with the direct and reflected ray paths as a function of satellite elevation angle, latitude, and solar activity. Simulation using the International Reference Ionosphere 2007 shows that differential ionospheric content is inversely proportional to satellite elevation angle and that low latitudes present larger ionospheric impacts on the reflected signals. Finally, high solar activity conditions lift the ionospheric density profile to and possibly above the CYGNSS orbit altitude of 500 km. The ionospheric delay will not generally affect the estimation of wind speed but may affect the CYGNSS signal acquisition and open loop tracking process. Implications of the ionospheric delay in other GNSS reflectometry applications are also discussed.
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    Feature Extraction Using Weighted Training Samples (2015)
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    Publication Date: 2015-03-27
    Description: Feature extraction using weighted training (FEWT) samples is proposed in this letter. Different spectral bands (features) play different roles in identification of land-cover classes. In the FEWT, the relative importance of each feature of a training sample in predicting the class label of that sample is obtained and considered as a weight for that feature. Then, the weighted training samples can be used in each arbitrary feature extraction method. In this letter, we use the weighted training samples in supervised locality preserving projection. The experimental results on three popular hyperspectral images show that FEWT has better performance and more speed than some state-of-the-art supervised feature extraction methods using limited number of available training samples.
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    Fast Multifeature Joint Sparse Representation for Hyperspectral Image Classification (2015)
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    Publication Date: 2015-03-27
    Description: Since hyperspectral images (HSIs) usually have complex content and chaotic background, multiple kinds of features would be helpful for the classification task. Recently, representation-based methods with multifeature combination learning have been proposed. However, multifeature learning and the extended contextual information require much more computational burden, particularly for a large-scale dictionary case. In this letter, we propose a fast joint sparse representation classification method with multifeature combination learning for hyperspectral imagery. Once getting several complementary features (spectral, shape, and texture), the proposed model simultaneously acquires a representation vector for each kind of feature and imposes the joint sparsity $boldsymbol{ell}_{mathrm{row},0} $ -norm regularization on the representation coefficients. The regularization can enforce the coefficients to share a common sparsity pattern, which preserves the cross-feature information. A new version of the simultaneous orthogonal matching pursuit is presented to solve the aforementioned problem because of its optimization with strong convergence guarantee and efficiency. Moreover, to further improve the classification performance, we incorporate contextual neighborhood information of the image into each kind of feature. Compared with state-of-the-art algorithms, it has been proved that the proposed algorithm with much less memory requirements performs tens to hundreds of times faster than those on real HSIs, while providing the same (or even better) accuracy.
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    Advanced Radar Geosynchronous Observation System: ARGOS (2015)
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    Publication Date: 2015-03-27
    Description: The Advanced Radar Geosynchronous Observation System is proposed to be a multiple-input–multiple-output synthetic aperture radar (SAR) system hosted on a swarm of minisatellites in quasi-geostationary orbits. The system is made of $N$ iso-frequency sensors, each of them transmitting and receiving the signals. The system would combine the continuous imaging capabilities of a geostationary SAR, gaining a factor $N^{2}$ in signal-to-noise ratio (SNR). The real aperture would be achievable in $sim$ 40 min, enabling applications so far unseen, such as monitoring fast deformations, landslides, and other applications for emergency and security. Still, the SNR of the long acquisition time would be conserved. The optimal design of the swarm is addressed, in order to trade resolution, coverage, and revisit time.
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    IEEE Geoscience and Remote Sensing Letters publication information (2016)
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    Publication Date: 2016-03-25
    Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.
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    Wavelet-Compressed Representation of Landscapes for Hydrologic and Geomorphologic Applications (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-03-25
    Description: The availability of high-resolution digital elevation data (submeter resolution) from LiDAR has increased dramatically over the past few years. As a result, the efficient storage and transmission of those large data sets and their use for geomorphic feature extraction and hydrologic/environmental modeling are becoming a scientific challenge. This letter explores the use of multiresolution wavelet analysis for compression of LiDAR digital elevation data sets. The compression takes advantage of the fact that, in most landscapes, neighboring pixels are correlated and thus contain some redundant information. The space–frequency localization of the wavelet filters allows one to preserve detailed high-resolution features where needed while representing the rest of the landscape at lower resolution. We explore a lossy compression methodology based on biorthogonal wavelets and demonstrate that, by keeping only approximately 10% of the original information (data compression ratio ∼94%), the reconstructed landscapes retain most of the information of relevance to geomorphologic applications, such as the ability to accurately extract channel networks for environmental flux routing, as well as to identify geomorphic process transition from the curvature–slope and slope–distance relationships.
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    Active–Passive Soil Moisture Retrievals During the SMAP Validation Experiment 2012 (2016)
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    Publication Date: 2016-03-25
    Description: The goal of this study is to assess the performance of the active–passive algorithm for the NASA Soil Moisture Active Passive mission (SMAP) using airborne and ground observations from a field campaign. The SMAP active–passive algorithm disaggregates the coarse-resolution radiometer brightness temperature ( $TB$ ) using high-resolution radar backscatter $(sigma^o)$ observations. The colocated $TB$ and $sigma^o$ acquired by the aircraft-based Passive Active L- and S-band sensor during the SMAP Validation Experiment 2012 (SMAPVEX12) are used to evaluate this algorithm. The estimation of its parameters is affected by changes in vegetation during the campaign. Key features of the campaign were the wide range of vegetation growth and soil moisture conditions during the experiment period. The algorithm performance is evaluated by comparing retrieved soil moisture from the disaggregated brightness temperatures to in situ soil moisture measurements. A minimum performance algorithm is also applied, where the radar data are withheld. The minimum performance algorithm serves as a benchmark to asses the value of the radar to the SMAP active–passive algorithm. The temporal correlation between ground samples and the SMAP active–passive algorithm is improved by 21% relative to minimum performance. The unbiased root-mean-square error is decreased by 15% overall.
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    Experimental Low-Terahertz Radar Image Analysis for Automotive Terrain Sensing (2016)
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    Publication Date: 2016-03-25
    Description: In this letter, we report initial experimental results which provide the foundation for low-terahertz (low-THz) radar imagery for outdoor unstructured scenarios as expected in automotive sensing. The requirements and specifications for a low-THz single imaging radar sensor are briefly outlined. The imaging capabilities of frequency-modulated continuous-wave (FMCW) radar operating at 150 GHz are discussed. A comparison of experimental images of on-road and off- road scenarios made by a 150-GHz FMCW radar and a reference 30-GHz stepped-frequency radar is implemented, and their performance is analyzed.
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    Problematic Projection to the In-Sample Subspace for a Kernelized Anomaly Detector (2016)
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    Publication Date: 2016-03-25
    Description: We examine the properties and performance of kernelized anomaly detectors, with an emphasis on the Mahalanobis-distance-based kernel RX (KRX) algorithm. Although the detector generally performs well for high-bandwidth Gaussian kernels, it exhibits problematic (in some cases, catastrophic) performance for distances that are large compared to the bandwidth. By comparing KRX to two other anomaly detectors, we can trace the problem to a projection in feature space, which arises when a pseudoinverse is used on the covariance matrix in that feature space. We show that a regularized variant of KRX overcomes this difficulty and achieves superior performance over a wide range of bandwidths.
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    A Novel MRF-Based Multifeature Fusion for Classification of Remote Sensing Images (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-03-25
    Description: The spatial information has been proved to be effective in improving the performance of spectral-based classification. However, it is difficult to describe different image scenes by using monofeature owing to complexity of the geospatial scenes. In this letter, a novel framework is developed to combine the multiple spectral and spatial features based on the Markov random field (MRF). Specifically, the pixels in an image are separated into reliable and unreliable ones according to the decision of multifeature classifications. The labels of the reliable pixels can be conveniently determined, but the unreliable pixels are then classified by fusing the multifeature classification results and reducing the classification uncertainties based on the MRF optimization. Experiments are conducted on three multispectral high-resolution images to verify the effectiveness of the proposed method. Several state-of-the-art multifeature classification methods are also achieved for the purpose of comparison. Moreover, three classifiers (i.e., multinomial logistic regression, support vector machines, and random forest) are used to test the performance of the proposed framework. It is shown that the proposed method can effectively integrate multiple features, yield promising results, and outperform other approaches compared.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 96
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    Modified Levenberg–Marquardt-Based Optimization Method for LiDAR Waveform Decomposition (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-03-25
    Description: A modified Levenberg–Marquardt (LM) method is proposed to improve the waveform-decomposition efficiency of light detection and ranging (LiDAR). The conventional constant-model-based LM fitting algorithm is subsequently modified using two proposed models: the linear model and exponential model. By revising the update coefficient of the damping term to make it consistent with the variation of residual error, the magnitude of oscillation is effectively reduced to provide better convergence. The models were experimentally verified using observed data acquired by our experimental large-footprint LiDAR system. The results indicate that the two modified LM-based algorithms provide better performance in terms of convergence speed and iteration efficiency for waveform decomposition in comparison with the traditional algorithm. Most prominently, the exponential LM algorithm provides 69% maximum improvement in convergence speed and 103% in acceptable iteration efficiency in comparison with the traditional algorithm.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 97
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    An Efficient General Algorithm for SAR Imaging: Complex Approximate Message Passing Combined With Backprojection (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-03-25
    Description: Due to the great computation load and memory cost of the matrix-vector multiplication, the sparse reconstruction algorithms are severely limited in the applications of radar imaging with real data. In order to solve this problem, we construct a backprojection-based range–azimuth decoupled operator (BP-RADOp) and combine the complex approximate message passing algorithm (CAMP) with it. We call this algorithm BP-CAMP in this letter. Since BP-RADOp retains the merits of the backprojection method entirely (i.e., perfect motion compensation for any flight path, precise focus for arbitrarily wide bandwidths and integration angles, low artifact levels, unlimited scene size, and strictly local processing), it has universal applicability in comparison with the other decoupled operators deduced from the fast Fourier transform-based image formation algorithms. The theoretical analysis indicates when BP-CAMP and CAMP are both used to reconstruct large-scale observed scenes; the former has lower computation load and memory cost than the latter. Meanwhile, it is demonstrated that BP-CAMP achieves high-quality synthetic aperture radar imaging with undersampled echo data, and it is as robust as CAMP to additive noise by the simulations and real data processing.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 98
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    High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-03-25
    Description: Satellite-retrieved aerosol optical depth (AOD) has been increasingly utilized for the mapping of fine particulate matter (PM 2.5 ) concentrations. An accurate estimation and mapping of PM 2.5 concentrations depends on the high-resolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM 2.5 and AOD. Take the core portion of the Beijing–Hebei–Tianjin (Jing-Jin-Ji) urban agglomeration as case study (the most seriously polluted region in China). Land use, population, meteorological variables, and simplified aerosol retrieval algorithm-retrieved AOD at 1-km resolution are employed as the predictors for the geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM 2.5 concentrations. The GWR model shows significant spatial variations in PM 2.5 concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with ground-level PM 2.5 concentrations demonstrates that PM 2.5 concentrations predicted by the GWR model ( $R^{2} = 0.75$ , $RMSE = 10 mutext{g/m}^{3}$ ) correlate better than those by the OLS model ( $R^{2} = 0.53$ , $RMSE = 16 mutext{g/m}^{3}$ ). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM 2.5 concentrations over urban areas.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 99
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    Pole-Like Road Object Detection in Mobile LiDAR Data via Supervoxel and Bag-of-Contextual-Visual-Words Representation (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-03-25
    Description: This letter addresses the problem of detecting pole-like road objects (including light poles and traffic signposts) from mobile light detection and ranging (LiDAR) data for transportation-related applications. The method consists of two consecutive stages: training and pole-like object detection. At the training stage, a contextual visual vocabulary is created from the feature regions generated from a training data set by supervoxel segmentation. At the pole-like object detection stage, a bag-of-contextual-visual-words representation is generated for each semantic object segmented from mobile LiDAR data. The experimental results show that the proposed method achieves correctness, omission, and commission of 88.9%, 11.1%, and 2.8%, respectively, in detecting pole-like road objects. Computational complexity analysis demonstrates that our method provides a promising and effective solution to rapid and accurate detection of pole-like objects from large volumes of mobile LiDAR data.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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    Sea Surface Reflectivity Variation With Ocean Temperature at Ka-Band Observed Using Near-Nadir Satellite Radar Data (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-03-25
    Description: Satellite ocean radar data are used to assess the flat surface reflectivity for seawater at 36 GHz by comparison to an existing model for dielectric constant variation. Sea surface temperature (SST) is the dominant control, and results indicate a 14% variation in the normalized radar cross section (NRCS) at Ka-band (35.75 GHz) that is in close agreement with model prediction. Consistent results are obtained globally using near-nadir incidence data from both the SARAL AltiKa radar altimeter and Global Precipitation Measurement mission rain radar. The observations affirm that small but systematic SST-dependent corrections at Ka-band may require consideration prior to NRCS use in ocean surface wave investigations and applications. As an example, we demonstrate a systematic improvement in AltiKa ocean wind speed inversions after such an SST adjustment. Lower frequency C- and Ku-band results are also assessed to confirm the general agreement with prediction and a much smaller variation due to SST.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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