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  • Articles  (314)
  • Institute of Electrical and Electronics Engineers (IEEE)  (314)
  • Public Library of Science (PLoS)
  • 2015-2019  (314)
  • 1945-1949
  • 2015  (314)
  • IEEE Geoscience and Remote Sensing Letters  (314)
  • 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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    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)
    Institute of Electrical and Electronics Engineers (IEEE)
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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 (2015)
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    Publication Date: 2015-12-29
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    Introducing IEEE Collabratec (2015)
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    Publication Date: 2015-12-29
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    IEEE Geoscience and Remote Sensing Letters institutional listings (2015)
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    Publication Date: 2015-12-29
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    IEEE Geoscience and Remote Sensing Letters information for authors (2015)
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    Publication Date: 2015-12-29
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    A SAR Autofocus Technique With MUSIC and Golden Section Search for Range Bins With Multiple Point Scatterers (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-17
    Description: Many synthetic aperture radar (SAR) autofocus techniques use range bins containing a single dominant point scatterer to estimate the phase error by maximizing or minimizing an objective function. We analytically show that some widely used objective functions do not give accurate phase error estimates if the objective function is constructed using a range bin containing multiple strong point scatterers (SPSs). Multiple SPSs are often observed in the range bins extracted from SAR images of urban areas containing many bright man-made objects. Such multiple SPSs do not allow us to obtain accurate estimates due to the interference between SPSs. To overcome this multiple scatterer problem, we propose the use of a combined entropy objective function with the local magnitudes of SPSs, along with the multiple-signal classification algorithm. Our experiment with actual SAR data confirms the superiority of the proposed approach.
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    Downscaling GOES Land Surface Temperature for Assessing Heat Wave Health Risks (2015)
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    Publication Date: 2015-06-17
    Description: Recent years have witnessed an emerging concern of the health impact of heat waves. A common approach to investigate heat waves is to resort to the geostationary thermal infrared imagery, such as those from the Geostationary Operational Environmental Satellite (GOES) and Meteosat Second Generation. However, coarse spatial resolutions of geostationary images cannot meet the need of assessing and monitoring heat waves in complex urban settings. To address the spatial and temporal variability of heat waves in urban areas, this letter presented a study of analyzing heat wave risk in Los Angeles, USA, by the synergistic use of GOES land surface temperature (LST), auxiliary geospatial, and census data within the framework of Crichton's Risk Triangle (i.e., hazard, exposure, and vulnerability). Principal component analysis and regression analysis were employed to downscale the original GOES LST imagery from 4 to 1 km. The resultant subhourly 1-km LST data was used to characterize and quantify heat hazard. The census population represented the exposure, while existing health, socioeconomic, and physical environmental conditions were used to describe the vulnerabilities. The risk map of heat wave was computed using the weighted indices of hazard, exposure, and vulnerability. The map was further overlaid with a zip-code data layer to generate statistics. The derived risk map showed that areas with high risk were identified in the central city, part of western LA County, and the desert area, based on a 10-point scale rank.
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    Identifying Surface Materials on an Active Volcano by Deriving Dielectric Permittivity From Polarimetric SAR Data (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-17
    Description: Dielectric permittivity $varepsilon_r$ measured on the Earth's surface is an effective property for characterizing surface materials in terms of rock type and water content, particularly in highly changeable environments such as active volcanoes. We propose a technique termed dielectric permittivity from polarimetric synthetic aperture radar (dPSAR) to quantify $varepsilon_r$ using a single scene of polarimetric SAR data, based on the small perturbation model of backscattering (SPMB) . For an optimal solution, the Nelder–Mead simplex method was combined with SPMB. The application of dPSAR to a scene of ALOS PALSAR data from the vicinity of Mt. Merapi, Indonesia, correctly identified the relative value ranges of $varepsilon_r$ for pyroclastic flow and tephra deposits accompanying large eruptions that occurred on November 5, 2010; their means were 2.55 and 3.07, respectively. Pore water within porous ashes is a plausible factor for increases in the $varepsilon_r$ of the tephra.
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    An Iterative SAR Image Filtering Method Using Nonlocal Sparse Model (2015)
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    Publication Date: 2015-06-17
    Description: In this letter, we propose an iterative synthetic aperture radar (SAR) image filtering method using the nonlocal sparse model. The original SAR image is first transformed to the logarithmic SAR image domain. Then, we use the nonlocal sparse model and the iterative regularization technique to denoise the log-intensity image. In each iteration, we update the noisy image and then estimate the noise variance. For each patch in the noisy image, we find several similar patches and stack them together in a group. This noisy group is filtered by simultaneous sparse coding. Then, all of the filtered groups are aggregated to form the denoised image. Experimental results demonstrate that the proposed method can achieve state-of-the-art SAR image despeckling performance.
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    Region-Based Classification of SAR Images Using Kullback–Leibler Distance Between Generalized Gamma Distributions (2015)
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    Publication Date: 2015-06-17
    Description: For the classification of synthetic aperture radar (SAR) images, traditional pixel-based Bayesian classifiers suffer from an intrinsic flaw that categories with serious overlapped probability density functions cannot be well classified. To solve this problem, in this letter, a region-based classifier for SAR images is proposed, where regions, instead of individual pixels, are treated as elements for classification. In the algorithm, each region is assigned to the class that minimizes a criterion referring to the Kullback–Leibler distance. Besides, the generalized gamma distribution $(mathrm{G}Gammamathrm{D} ) $ , a flexible empirical model, is employed for the statistical modeling of SAR images. Finally, with a synthetic image and an actual SAR image acquired by the EMISAR system, the effectiveness of the proposed algorithm is validated, compared with the pixel-based maximum-likelihood method and two region-based Bayesian classifiers.
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    Geolocalization of Crowdsourced Images for 3-D Modeling of City Points of Interest (2015)
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    Publication Date: 2015-06-17
    Description: Geolocalization of crowdsourced images is a challenging task that is getting increased attention nowadays due to the rise in popularity of geotagging and its applications. Among these applications, 3-D modeling from Internet photograph collections is a very active research topic with great promise and potential. In order to automize and optimize the crowdsourced 3-D modeling process, this letter proposes a novel framework that can be used for automatic 3-D modeling of city points of interest (POIs), such as statues, buildings, and temporary artworks. Crowdsourced images related to the POI and its location are collected using a geographical Web search process based on geotags and semantic geodata. Subsequently, panoramic Google Street View (SV) images are used to geolocalize the images. If enough feature matches are found between the image and one of the SV images, the image is annotated with the location metadata of the best matching image from the SV database. Otherwise, when too few matches are found, the image most probably will not contain the POI in its field of view (FOV), and it is filtered out. For optimal performance, the equirectangular panoramic SV images are transformed into an SV data set of perspective cutouts facing the POI with different pitches and FOVs. From this data set, a basic 3-D model of the POI and its environment is generated. Finally, the geolocalized crowdsourced images refine and optimize the 3-D model using the matching matrix that is generated from the geolocalization results. Experiments show the feasibility of our approach on different types of city POIs. Our main contribution is that we can decrease the 3-D modeling computation time by more than half and significantly improve the model completeness. Finally, it is important to remark that the applicability of the proposed framework is not limited to 3-D modeling but can also be used in other domains, such as geoaugmented reality and location-based media annotation.
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    SAR Ship Detection and Self-Reporting Data Fusion Based on Traffic Knowledge (2015)
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    Publication Date: 2015-06-17
    Description: The improvement in Maritime Situational Awareness, the capability of understanding events, circumstances, and activities within and impacting the maritime environment, is nowadays of paramount importance for safety and security. The integration of spaceborne synthetic aperture radar (SAR) data and automatic identification system (AIS) information has the appealing potential to provide a better picture of what is happening at sea by detecting vessels that are not reporting their positioning data or, on the other side, by validating ships detected in satellite imagery. In this letter, we propose a novel architecture that is able to increase the quality of SAR/AIS fusion by exploiting knowledge of historical vessel positioning information. Experimental results are presented, testing the algorithm in the specific area of Dover Strait using real SAR and AIS data.
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    Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral Images (2015)
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    Publication Date: 2015-06-17
    Description: In this letter, we explore the use of self-dual attribute profiles (SDAPs) for the classification of hyperspectral images. The hyperspectral data are reduced into a set of components by nonparametric weighted feature extraction (NWFE), and a morphological processing is then performed by the SDAPs separately on each of the extracted components. Since the spatial information extracted by SDAPs results in a high number of features, the NWFE is applied a second time in order to extract a fixed number of features, which are finally classified. The experiments are carried out on two hyperspectral images, and the support vector machines and random forest are used as classifiers. The effectiveness of SDAPs is assessed by comparing its results against those obtained by an approach based on extended APs.
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    Learning With Hypergraph for Hyperspectral Image Feature Extraction (2015)
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    Publication Date: 2015-06-17
    Description: It is known that hyperspectral image (HSI) classification is a high-dimension low-sample-size problem. To ease this problem, one natural idea is to take the feature extraction as a preprocessing. A graph embedding model is a classic family of feature extraction methods, which preserves certain statistical or geometric properties of the data set. However, the graph embedding model considers only the pairwise relationship between two vertices, which cannot represent the complex relationships of the data. Utilizing the spatial structure of HSI, in this letter, we propose a spatial hypergraph embedding model for feature extraction. Experimental results demonstrate that our method outperforms many existing feature extract methods for HSI classification.
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    Laser Range Data Denoising via Adaptive and Robust Dictionary Learning (2015)
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    Publication Date: 2015-06-17
    Description: Sparse representation (SR) is making significant impact in the computer vision and signal processing communities due to its stunning performance in a variety of applications for images, e.g., denoising, restoration, and synthesis. We propose an adaptive and robust SR algorithm that exploits the characteristics of typical laser range data, i.e., the availability of both range and reflectance data, to realize range data denoising. Specifically, our method estimates the informative level (IL) of each patch according to the variation in both range and reflectance modalities, followed by adaptive dictionary training that assigns dynamic sparsity weights to the patches with different ILs. Furthermore, the $ell_1$ -norm-based representation fidelity measure is applied to make our method robust to outliers, which are common in laser range measurements. Extensive experiments on synthesized and actual data demonstrate that our method works effectively, resulting in superior performance both visually and quantitatively.
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    Effects of Polarization Distortion at Transmission and Faraday Rotation on Compact Polarimetric SAR System and $H/bar{alpha}$ Decomposition (2015)
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    Publication Date: 2015-06-17
    Description: In this letter, the influence of a polarization distortion at transmission and the Faraday rotation on compact polarimetric synthetic aperture radar (C-PolSAR) measurements is assessed. Polarization distortions at transmission for a C-PolSAR system can be introduced by nonideal transmission polarization waveforms, channel imbalance, and crosstalk. In addition, the Faraday rotation can also cause a distortion in a low-frequency spaceborne SAR system. The aforementioned two kinds of distortions have different influences on the measured data and the $H/bar{alpha} $ decomposition. This letter assesses the polarization quality of a C-PolSAR system and evaluates the various effects of errors on the compact polarimetric parameters, including polarimetric entropy $H$ and $bar{alpha} $ . Quality assessment and error analysis can be used to inform the C-PolSAR system design. Canadian AirSAR L-band data are used to assess the impact of the distortions on $H/bar{alpha}$ .
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    Semisupervised Discriminant Analysis for Hyperspectral Imagery With Block-Sparse Graph (2015)
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    Publication Date: 2015-06-17
    Description: In this letter, a semisupervised block-sparse graph is proposed for discriminant analysis of hyperspectral imagery. To overcome the difficulty of not having enough training samples in the previously developed block-sparse graph approach, unlabeled samples are selected to participate in graph construction. Both sparse and collaborative representations are used for unlabeled sample selection. The experimental results demonstrate that the proposed semisupervised block-sparse graph can significantly outperform the supervised version with limited training samples. The sparse and collaborative representation-based selection methods perform comparably with the collaborative version requiring much lower computational cost.
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    Absolute Height Estimation Using a Single TerraSAR-X Staring Spotlight Acquisition (2015)
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    Publication Date: 2015-06-17
    Description: The work presented in this letter exploits the long synthetic aperture radar (SAR) of a single TerraSAR-X Staring Spotlight (ST) acquisition to derive absolute heights. Here, the slight azimuth defocusing effect due to height mismatch between the true height and the height assumed in SAR focusing is analyzed. The impact is almost negligible for most of acquisition modes. In contrast, spaceborne modes with very long aperture, such as the TerraSAR-X ST acquisition mode, present sensibility that can be used to retrieve absolute heights. The accuracy depends on incidence angle, orbit type, and mainly on signal-to-clutter ratio. Two different results are presented to demonstrate that absolute heights can be retrieved with accuracy of few meters using a single TerraSAR-X ST acquisition.
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    Quadruple Collocation Analysis for Soil Moisture Product Assessment (2015)
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    Publication Date: 2015-06-17
    Description: For validating remotely sensed products, the triple collocation (TC) is often adopted, which is able to retrieve the independent error variances of three systems observing the same target parameter. In this letter, three years of soil moisture data derived from the Advanced SCATterometer (ASCAT) aboard the MetOp satellite and the Soil Moisture and Ocean Salinity (SMOS) radiometer are analyzed and compared with the ERA Interim/Land model outputs and the ground measurements available from the International Soil Moisture Network. As we have four sources, a novel quadruple collocation (QC) approach is developed, which is more precise than TC since it uses the sources jointly. The results of QC show that the ERA model has the lowest error variance, while ground measurements are likely to be affected by the difficulty to represent a mean soil moisture within the satellite field of view by a limited number of stations. Moreover, the ASCAT retrievals outperform the SMOS ones if only anomalies with respect to the seasonal trend are considered, while the opposite occurs when the whole dynamic of soil moisture variation is considered.
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    GPR Signal Denoising and Target Extraction With the CEEMD Method (2015)
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    Publication Date: 2015-06-17
    Description: In this letter, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by empirical mode decomposition (EMD) applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode-mixing problem in the EMD method and improve the resolution of ensemble EMD (EEMD) when the signal has a low signal-to-noise ratio. First, we analyze the difference between the basic theory of EMD, EEMD, and CEEMD. Then, we compare the time and frequency analysis with Hilbert–Huang transform to test the results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral–spatial resolution than the other two EMD methods in GPR signal denoising and target extraction. Its decomposition is complete, with a numerically negligible error.
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    A Novel Helicopter-Borne Rotating SAR Imaging Model and Algorithm Based on Inverse Chirp-Z Transform Using Frequency-Modulated Continuous Wave (2015)
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    Publication Date: 2015-06-17
    Description: With an appropriate geometric configuration, a helicopter-borne rotating synthetic aperture radar (ROSAR) can break through the limitations of conventional strip-map monostatic SAR on forward-looking imaging. Owing to such a capability, ROSAR has extensive potential applications, such as self-navigation and self-landing. Moreover, it has many advantages if combined with frequency-modulated continuous wave (FMCW) technology. In this letter, a novel geometric platform configuration and an imaging algorithm for helicopter-borne FMCW-ROSAR are proposed. First, by adopting the higher order approximation of slant range model to improve the azimuth resolution for FMCW-ROSAR, the precise 2-D spectrum of the echo signal is derived based on series reversion. Moreover, at the same time, the Doppler offset caused by the continuous motion of the antenna is analyzed and compensated as well. Then, according to the analysis on the range-dependent velocity variation caused by ROSAR geometric configuration, an efficient inverse chirp-Z transform is utilized to remove the variant range cell migration, and a well-focused SAR image can thus be obtained. Finally, the experimental results with simulated data demonstrate the effectiveness of the proposed algorithm.
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    Radio-Frequency Interference Identification Over Oceans for C- and X-Band AMSR2 Channels (2015)
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    Publication Date: 2015-06-17
    Description: A new method for radio-frequency interference (RFI) contamination identification over open oceans for the two C-subbands and X-band of Advanced Microwave Scanning Radiometer 2 (AMSR2) channel measurements is suggested. The method is based both on the AMSR2 brightness temperature ( $T_{B} $ ) modeling and on the analysis of AMSR2 measurements over oceans. The joint analysis of $T_{B} $ spectral differences allowed to identify the relations between them and the limits of their variability, which are ensured by the changes in the environmental conditions. It was found that the constraints, based on the ratio of spectral differences, are more regionally and seasonally independent than the spectral differences themselves. Although not all possible RFI combinations are considered, the developed simple criteria can be used to detect most RFI-contaminated pixels over the World Ocean for AMSR2 measurements in two C-subbands and the X-band.
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    Interferometric Phase Denoising by Median Patch-Based Locally Optimal Wiener Filter (2015)
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    Publication Date: 2015-06-17
    Description: This letter presents a new filtering technique for interferometric synthetic aperture radar (InSAR) phase images. Traditional local denoising algorithms all suffer from the drawback of removing texture detail information. In contrast, several nonlocal methods have attained good performance in InSAR applications. However, these methods only take radiometric similarity. The patch-based locally optimal Wiener filter (PLOW) utilizes both geometrically and radiometrically similar patch information by clustering analysis and nonlocal filtering; thus, it can better balance between detail preservation and denoising. Nevertheless, PLOW itself is not fitted for InSAR. In this letter, we modify and improve the original algorithm while considering the coherence coefficient and the characteristics of InSAR. This new method provides better filtering results, but with a higher computational cost. Moreover, we introduce the box dimension in fractal geometry as a new index. Experiments on simulated and real data demonstrate that this algorithm outperforms traditional denoising methods.
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    A New Approach to Segmentation of Multispectral Remote Sensing Images Based on MRF (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-17
    Description: Segmentation of multispectral remote sensing images is a key competence for a great variety of applications. Many of the applied segmentation algorithms are generative models based on Markov random fields. These approaches are generally limited to multivariate probability densities such as the normal distribution. In addition, it is usually impossible to adjust the contextual parameters separately for each frequency band. In this letter, we present a new segmentation algorithm that avoids the aforementioned problems and allows the use of any univariate density function as emission probability in each band. The approach consists of three steps: first, calculate feature vectors for every frequency band; second, estimate contextual parameters for every band and apply local smoothing; and third, merge the feature vectors of the frequency bands to obtain final segmentation. This procedure can be iterated; however, experiments show that after the first iteration, most of the pixels are already in their final state. We call our approach successive band merging (SBM). To evaluate the performance of SBM, we segment a Landsat 8 and an AVIRIS image. In both cases, the $widehat{kappa} $ coefficients show that SBM outperforms the benchmark algorithms.
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    A Fully Embedded Two-Stage Coder for Hyperspectral Near-Lossless Compression (2015)
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    Publication Date: 2015-06-17
    Description: This letter proposes a near-lossless coder for hyperspectral images. The coding technique is fully embedded and minimizes the distortion in the $l_2$ -norm initially and in the $l_infty$ -norm subsequently. Based on a two-stage near-lossless compression scheme, it includes a lossy and a near-lossless layer. The novelties are the observation of the convergence of the entropy of the residuals in the original domain and in the spectral–spatial transformed domain and an embedded near-lossless layer. These contributions enable a progressive transmission while optimizing both signal-to-noise ratio (SNR) and peak absolute error (PAE) performance. The embeddedness is accomplished by bitplane encoding plus arithmetic encoding. Experimental results suggest that the proposed method yields a highly competitive coding performance for hyperspectral images, outperforming multicomponent JPEG2000 for the $l_infty$ -norm and pairing its performance for the $l_2$ -norm, and also outperforming M-CALIC in the near-lossless case, i.e., for $mbox{PAE}ge 5$ .
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    Class-Specific Feature Selection With Local Geometric Structure and Discriminative Information Based on Sparse Similar Samples (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-03-27
    Description: It is necessary while quite challenging to select features strongly relevant to a thematic class, i.e., class-specific features, from very high resolution (VHR) remote sensing images. To meet this challenge, a class-specific feature selection method based on sparse similar samples (CFS4) is proposed. Specifically, CFS4 incorporates the local geometrical structure and discriminative information of the data into a sparsity regularization problem. The experimental results on VHR satellite images well validate the effectiveness and practicability of the proposed method.
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    Impacts of Wall–Target Interaction on Matched Illumination Waveforms for TWRI (2015)
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    Publication Date: 2015-03-27
    Description: The impacts of wall–target interaction on matched illumination waveforms for through-the-wall radar imaging are examined via finite-difference time-domain simulation. Returns from various wall–target scenarios are considered as a function of the target-to-wall separation in order to examine the effectiveness of the so-called primary-wave target response in the matched illumination implementation. The primary-wave target response is shown to effectively maximize the signal-to-interference-and-noise ratio (SINR) in through-wall radar applications where the wall–target interaction is minor, and the primary-wave response closely resembles the full-wave target response, which contains all wall–target interactions. The ability of the primary-wave target response to maximize the SINR can be degraded by relatively minor errors in the wall–target transfer function caused by the incomplete wall–target physics inherent to the scheme. In such cases, the resulting matched illumination waveform spectrum is generally characterized by narrowband energy concentrated at suboptimal frequencies.
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    Unsupervised Hyperspectral Image Band Selection via Column Subset Selection (2015)
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    Publication Date: 2015-03-27
    Description: In this letter, we proposed a novel band selection algorithm for hyperspectral images (HSIs) based on column subset selection. The main idea of the proposed algorithm comes from the column subset selection problem in numerical linear algebra. It selects a group of bands, which maximizes the volume of the selected subset of columns. Since the high dimensionality decreases the contrast between bands, we use Manhattan distance to obtain a higher selection quality. Experimental results on real HSIs show that the proposed algorithm obtains competitively good results, in terms of classification accuracy, and is robust to noisy bands.
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    A Uniform SIFT-Like Algorithm for SAR Image Registration (2015)
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    Publication Date: 2015-03-27
    Description: In this letter, a uniform scale-invariant feature transform (SIFT)-like algorithm is proposed for synthetic aperture radar (SAR) image registration, which can extract enough robust, reliable, and uniformly distributed features by the strategies of optimal feature selection based on a Voronoi diagram and feature scale-space proportional extraction. SAR images, taken from different viewpoints by an airborne sensor and at different times by spaceborne sensors, were used as test data to validate the effectiveness of the proposed algorithm. The indexes of local density and global coverage were used to assess the spatial distribution of matches. Compared with the traditional SIFT-like algorithm for SAR images (SAR-SIFT), the results show that the proposed algorithm can increase the number of matches and optimize their spatial distribution.
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    Improving Backscatter Intensity Calibration for Multispectral LiDAR (2015)
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    Publication Date: 2015-03-27
    Description: A wavelength-dependent light detection and ranging (LiDAR) backscatter intensity calibration method was developed to maximize the advantages of a multispectral LiDAR system. We established a spectral ratio calibration method for multispectral LiDAR and investigated the effective calibration procedure for the mixed measurement of the effect of incident angle and surface roughness. Experiment results showed that the proposed LiDAR spectral ratio is insensitive to sensor-related factors and advantageous in calibrating the effect of incidence angle and surface roughness. As the product of the LiDAR calibration procedure based on spectral ratio, extended vegetation indexes significantly improve the classification accuracy.
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    HY-2A Altimeter Time Tag Bias Estimation Using Reconstructive Transponder (2015)
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    Publication Date: 2015-03-27
    Description: Independent clocks provide time tags for the precision orbit determination (POD) equipment and the radar altimeter onboard the HY-2A satellite, and a bias between POD data' time tag and corresponding range observation's time tag from the HY-2A altimeter exists. The time tag bias contributes a bias in the sea surface height observation due to the nonzero time rate of change of the HY-2A altimeter's height. A transponder for in-orbit radar altimeter calibration provides an approach to estimate the time tag bias. The altimeter receives the responding signals from the transponder and generates ranges. Pertinent reference ranges are obtained from the POD data and the transponder's coordinate. Using the ranges from the radar altimeter and the reference ranges, the time tag bias between the POD data and the altimeter observations can be estimated. During an in situ HY-2A altimeter calibration campaign using a reconstructive transponder from August 9, 2012, to July 20, 2014, 17 estimations of the altimeter's time tag bias were obtained. The preliminary results are presented in this letter.
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    IEEE Geoscience and Remote Sensing Letters publication information (2015)
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    Publication Date: 2015-10-27
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    Quantifying Libya-4 Surface Reflectance Heterogeneity With WorldView-1, 2 and EO-1 Hyperion (2015)
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    Publication Date: 2015-10-27
    Description: The land surface imaging (LSI) virtual constellation approach promotes the concept of increasing Earth observations from multiple but disparate satellites. We evaluated this through spectral and spatial domains, by comparing surface reflectance from 30-m Hyperion and 2-m resolution WorldView-2 (WV-2) data in the Libya-4 pseudoinvariant calibration site. We convolved and resampled Hyperion to WV-2 bands using both cubic convolution and nearest neighbor (NN) interpolation. Additionally, WV-2 and WV-1 same-date imagery were processed as a cross-track stereo pair to generate a digital terrain model to evaluate the effects from large ( $〉$ 70 m) linear dunes. Agreement was moderate to low on dune peaks between WV-2 and Hyperion $(R^{2} 〈 0.4)$ but higher in areas of lower elevation and slope $(R^{2} 〉 0.6)$ . Our results provide a satellite sensor intercomparison protocol for an LSI virtual constellation at high spatial resolution, which should start with geolocation of pixels, followed by NN interpolation to avoid tall dunes that enhance surface reflectance differences across this internationally utilized site.
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    Introducing IEEE Collabratec (2015)
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    Publication Date: 2015-10-27
    Description: Advertisement, IEEE.
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    IEEE Geoscience and Remote Sensing Letters information for authors (2015)
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    IEEE Geoscience and Remote Sensing Letters institutional listings (2015)
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    Publication Date: 2015-10-27
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    Passive GNSS-Based SAR Resolution Improvement Using Joint Galileo E5 Signals (2015)
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    Publication Date: 2015-06-17
    Description: This letter demonstrates the feasibility of improving range resolution in passive synthetic aperture radar with the Galileo navigation satellites as transmitters of opportunity. This can be done by an appropriate coherent combination of the Galileo E5a and E5b signals. The proposed approach is analytically derived and is confirmed both by simulation and experiments.
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    Improved Azimuth Multichannel SAR Imaging for Configurations With Redundant Measurements (2015)
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    Publication Date: 2015-06-17
    Description: The traditional spectrum reconstruction algorithm for nonuniform sampling of a displaced phase center antenna (DPCA) synthetic aperture radar (SAR) system can provide an unambiguous recovery of an original signal through the inverse computation of a joint filter matrix. However, when the sampling positions of different receivers coincide spatially, the joint filter matrix will be non-invertible, thus causing the ineffectiveness of the algorithm. This letter proposes a regularized spectrum reconstruction scheme when the pulse repetition frequency of a multichannel system operates on these critical frequencies, as well as in their neighborhoods. First, a low-order regularization of the joint filter matrix is suggested so that the original spectrum of the multichannel system can be recovered more accurately with a decreased number of nonredundant channels' measurements. Furthermore, in order to improve the output signal-to-noise ratio (SNR) of the reconstruction network, a combinational low-order spectrum recovery scheme is additionally presented, which takes all redundant channels' samples into account. The optimizations bring significant benefits to both the suppression of aliased ambiguities and the advance of the output SNR of the reconstruction network.
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    Improving Chlorophyll Fluorescence Retrieval Using Reflectance Reconstruction Based on Principal Components Analysis (2015)
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    Publication Date: 2015-06-17
    Description: Methods based on Fraunhofer line discrimination (FLD) for solar-induced chlorophyll fluorescence retrieval require making assumptions or estimations about true reflectance, which is a large source of error, particularly at the O 2 -B band. This letter presents an alternative solution, which is named the pFLD method, based on principal components analysis to reconstruct the reflectance spectra. The principal components were generated with simulated reflectance spectra covering the most common conditions of vegetation. The pFLD method has been tested with both simulated and field experiment data sets. Compared with the widely used three-band FLD and improved FLD methods, pFLD showed better performance at the O 2 -B band, particularly when the spectral resolution (SR) or the signal-to-noise ratio was relatively low. The pFLD method can be also successfully applied to field measurements with credible accuracy even at low SR.
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    Multicluster Spatial–Spectral Unsupervised Feature Selection for Hyperspectral Image Classification (2015)
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    Publication Date: 2015-06-17
    Description: A new unsupervised spatial–spectral feature selection method for hyperspectral images has been proposed in this letter. The key idea is to select the features that better preserve the multicluster structure of the multiple spatial–spectral features. Specifically, the multicluster structure information is obtained through spectral clustering utilizing a weighted combination of the multiple features. Then, such information is preserved in a group-sparsity-based robust linear regression model. The features that contribute more in preserving the multicluster structure information are selected. Comparative experiments on two popular real hyperspectral images validate the effectiveness of the proposed method, showing higher classification accuracy.
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    Assessing Long-Term Stability of SMOS Zero-Baseline Antenna Temperature Using the Aquarius Antenna Temperature Simulator (2015)
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    Publication Date: 2015-06-17
    Description: The National Aeronautics and Space Administration's Aquarius and the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) are satellite missions to make global L-band brightness temperature measurements. Aquarius uses a sophisticated antenna temperature simulator over oceans for the calibration of its brightness temperature measurements. In this investigation, the simulator was adapted to simulate the real aperture antenna temperature measured by the SMOS reference radiometers. It is found that the simulated antenna temperature is very close to the measured value. The analysis shows that the simulated antenna temperature can be utilized for SMOS calibration studies as an additional independent reference, as well as for investigating the consistency between SMOS and Aquarius brightness temperature measurements.
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    Evaluation of Topographic Correction on Subpixel Impervious Cover Mapping With CBERS-2B Data (2015)
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    Publication Date: 2015-06-17
    Description: This study compared the effectiveness of six commonly used topographic correction methods for subpixel impervious surface mapping in selected mountainous areas of Southwest Virginia. One 2008 China–Brazil Earth Remote Sensing 2B (CBERS-2B) image was processed using selected topographic algorithms and then used as input for subpixel impervious cover mapping. High-resolution National Agriculture Imagery Program (1-m resolution) images were used to build proportional subpixel impervious cover as training/validation data. We then applied a classification and regression tree algorithm to establish relationships between CBERS signals and impervious surfaces. Accuracy assessment showed that both $R^{2} $ (0.644–0.767) and RMSE (0.118–0.150, reported as proportion of impervious surface) values vary across different topographic correction algorithms. The accuracy differences ( $R^{2} $ : 0.448–0.771; RMSE: 0.118–0.247) were most pronounced for areas facing away from the sun azimuth angle, suggesting aspect–sun azimuth-dependent map accuracy. For terrain shadowing areas, the Minnaert method, the minslope method, and the C-correction substantially outperformed the cosine and improved cosine correction. These findings indicate that users should apply caution in using topographic correction algorithms and that aspect-stratified accuracy assessment needs to be conducted for detailed comparisons. We also repeated the analyses using Landsat TM and obtained better overall results compared to the CBERS-2B data. The differences in $R^{2} $ (or RMSE) for two data sources were not substantial, suggesting the high potential of CBERS data for subpixel impervious surface mapping.
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    Unstructured Versus Structured GLRT for Multipolarization SAR Change Detection (2015)
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    Publication Date: 2015-06-17
    Description: Coherent multipolarization synthetic aperture radar (SAR) change detection exploiting data collected from $N$ multiple polarimetric channels is addressed in this letter. The problem is formulated as a binary hypothesis testing problem, and a special block-diagonal structure for the polarimetric covariance matrix is forced to design a detector based on the generalized likelihood ratio test (GLRT) criterion. It is shown that the structured decision rule ensures the constant false alarm rate property with respect to the unknown disturbance covariance. Results on both simulated and real high-resolution SAR data show the effectiveness of the considered decision rule and its superiority against the traditional unstructured GLRT in some scenarios of practical interest.
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    About the Optimal Grid for SMOS Level 1C and Level 2 Products (2015)
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    Publication Date: 2015-06-17
    Description: Remotely sensed measurements acquired by the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite are processed in a uniform equal-area grid, the Icosahedral Snyder Equal Area (ISEA) 4H9. Brightness temperature measurements are projected onto that grid (the so-called Level 1C), as well as sea surface salinity and soil moisture estimates (Level 2). The ISEA grid has been chosen for its characteristics of equal area and almost uniform intercell spacing. Nevertheless, when considering the SMOS viewing geometry, the measurement footprint size, and the processing applied to those measurements, this choice may be revisited. With this objective, the ISEA 4H9 grid is compared to other equal-area grids with different sizes and orientations with respect to the satellite track. The best configuration resulted to be a 25-km-width grid symmetrical with respect to satellite track. This grid appeared to be better suited for improving SMOS Level 2 retrieval algorithms as well as to serve as input for higher level data production, since it best accounts for the instrument's viewing geometry and substantially reduces the correlation between adjacent grid cells.
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    Semisupervised Spectral–Spatial Classification of Hyperspectral Imagery With Affinity Scoring (2015)
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    Publication Date: 2015-06-17
    Description: Semisupervised classification has become popular, since it can make use of a limited amount of prior knowledge in hyperspectral images. However, spectral internal-class variability adds a great challenge to the task. To address these issues, we propose a novel semisupervised spectral–spatial classification method based on affinity scoring (AS) (SCAS). Adapted from fuzzy logic, AS exploits spectral and spatial features with their fuzzy contributions to classification by weighing on three factors: local class consistency, spectral similarity, and prior knowledge. SCAS consists of three main steps: oversegmentation, semisupervised classification, and modification. The first step generates superpixels and uses them to maintain local class consistency. The second and third steps employ AS to classify the superpixels and refine the classified map, respectively. Experiments show that the proposed method can outperform some classic methods and state-of-the-art classifiers.
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    Conductively Guided Borehole Radar Wave for Imaging Ahead of a Drill Bit (2015)
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    Publication Date: 2015-06-17
    Description: A guided wave is induced along a conductive logging cable or a conductive water-filled borehole during borehole radar (BHR) surveying. Such a guided wave is normally considered as unwanted interference to normal BHR waves and needs to be suppressed, either through BHR designs or data processing. In this letter, we advocate that the guided wave can be coupled onto a drill string, which can act as a forward-looking antenna for imaging ahead of the drill bit while drilling. Using data collected by a monostatic BHR with different configurations at an abandoned mine site in Brukunga, South Australia, we demonstrate that the forward-looking capability of the BHR is about 2–6 m in the tested borehole section.
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    Principal Component Reconstruction Error for Hyperspectral Anomaly Detection (2015)
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    Publication Date: 2015-06-17
    Description: In this letter, a reliable, simple, and intuitive approach for hyperspectral imagery (HSI) anomaly detection (AD) is presented. This method, namely, the global iterative principal component analysis (PCA) reconstruction-error-based anomaly detector (GIPREBAD), examines AD by computing errors (residuals) associated with reconstructing the original image using PCA projections. PCA is a linear transformation and feature extraction process commonly used in HSI and frequently appears in operation prior to any AD task. PCA features represent a projection of the original data into lower-dimensional subspace. An iterative approach is used to mitigate outlier influence on background covariance estimates. GIPREBAD results are provided using receiver-operating-characteristic curves for HSI from the hyperspectral digital imagery collection experiment. Results are compared against the Reed–Xiaoli (RX) algorithm, the linear RX (LRX) algorithm, and the support vector data description (SVDD) algorithm. The results show that the proposed GIPREBAD method performs favorably compared with RX, LRX, and SVDD and is both intuitively and computationally simpler than either RX or SVDD.
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    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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    On the Dependence of Delta-k Efficiency on Multilooking (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-17
    Description: This letter discusses some aspects of the implementation of Delta-k methods for shift estimation with synthetic aperture radar images. In particular, it shows that a common Delta-k algorithm, which postpones the multilooking to the differential interferogram and is therefore robust to the presence of interferometric fringes in the averaging window, does not reach the maximum possible performance and should be better considered as a variant of incoherent cross-correlation. A small adaptation, retaining some multilooking at interferogram level, can significantly improve the efficiency.
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  • 97
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    Use of Vegetation Change Tracker and Support Vector Machine to Map Disturbance Types in Greater Yellowstone Ecosystems in a 1984–2010 Landsat Time Series (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-17
    Description: Time series Landsat data have been used to track ecosystem disturbances using an algorithm such as the vegetation change tracker. However, efficiently identifying and separating types of disturbances (e.g., wildfires and harvests) still remain a technical challenge. In this letter, we tested the support vector machine algorithm in separating forest disturbance types, including wildfires, harvests, and other disturbance types (a generalized disturbance class, including insect disease outbreak, tornado, snow damage, and drought-induced mortality) in the Greater Yellowstone Ecosystem (GYE) using annual Landsat images from 1984 to 2010. The algorithm has been proven to be highly reliable, with overall accuracy about 87% for the study region. Average producer's and user's accuracy for wildfires and harvests were 85% and 90%, respectively. Based on the mapped forest disturbance type results, fire was the most dominant disturbance in GYE National Parks (NPs) from 1984 to 2010, affecting over 37% of the forested area in GYE NPs, whereas other disturbances such as insect and disease outbreaks were more frequent in national forests of the region during this time interval. With the free public access of the Landsat data and careful selection of training samples, this method can be useful in other ecosystems with similar disturbance dynamics as GYE.
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  • 98
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    Measurement and Correction of the Ionospheric TEC in P-Band ISAR Imaging (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-17
    Description: It is commonly known that the ionosphere has significant effects on a low-frequency (particularly P-band) radar signal. It causes the degradation of the image quality in synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging systems. In this letter, we analyze the ionospheric effects on radar signals and find that the total electron content (TEC) is a key to the ionospheric effects. A method is proposed to evaluate the TEC from a received ISAR signal and to correct the ionospheric effects. Some real experimental results, using a ground-based P-band ISAR system to observe a space target in the ionosphere, are used to validate the proposed method.
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  • 99
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    Image Fuzzy Clustering Based on the Region-Level Markov Random Field Model (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-17
    Description: The Markov random field (MRF) model serves as one of the most powerful tools to improve the robustness of fuzzy c-means (FCM) clustering. However, the use of a pixel-level MRF makes the clustering deficient to deal with images with macro texture patterns. In order to overcome such a problem, this letter presents a novel method that segments images by combining FCM with the region-level MRF (RMRF) model. In this method, a fuzzy novel energy function is established for the RMRF model and utilized in the process of fuzzy clustering, which plays an important role in describing large-range variations of macro textures. Considering the complexity of image textures, a region-level mean template is also established to enhance the relationships between neighboring regions in terms of spectral and structural information. Experiments are conducted using high-resolution remote sensing images, which demonstrate that the proposed method can improve the segmentation accuracy compared with four state-of-the-art competitors.
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    PolSAR Ship Detection Based on Superpixel-Level Scattering Mechanism Distribution Features (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-06-17
    Description: To improve the target detection performance under a low signal-to-clutter ratio, this letter presents a new polarimetric synthetic aperture radar (PolSAR) ship detector based on superpixel-level scattering mechanism (SM) distribution features. The proposed method is based on the observation that the SMs of targets and clutter have different distributions in the classical $H/alpha$ plane. To make use of this difference in ship detection, multiscale superpixels are first generated for PolSAR images. Then, two features describing the SM distribution in the superpixel are proposed. Based on these features, a test statistic independent of the scattering intensity is finally defined. The performance improvement of the proposed method is verified using a synthetic data set and real PolSAR images obtained from a RADARSAT-2 data set.
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