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  • 101
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: We address the problem of sea surface distribution modeling in a synthetic aperture radar (SAR) image by developing an innovative nonparametric method to tackle the main weakness of the traditional Parzen window kernel method, i.e., relatively low computation speed. We derive an explicit analytical solution of modeling sea surface distribution by a composite cubic Bézier curve and propose an adaptive segmentation strategy to improve the modeling precision. A comparative study validates that the average computation time of the proposed method is only 1/60 of the Parzen window kernel method and about 1/6 of the k-root and G0 methods. More importantly, in terms of modeling performance, the proposed method can achieve more adaptability and stability to different SAR sensors, resolutions, and sea scenes. The average goodness of fit tested on eight sea scenes of the proposed method, measured by $overline{|hat{R}^{2}|}$ (the smaller the better), is only 0.0006 and outperforms that of the Parzen window kernel method (0.0059), k-root (0.0390), and G0 (0.0678).
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  • 102
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: Conventional line detection methods are mainly based on the binary edge map. This letter proposes a new line detection method that directly extracts line features from the image edge fields of the synthetic aperture radar (SAR) images. In the proposed method, the strength and direction of each field point are first obtained using a ratio-based edge filter. Then, the accumulation weight of the field point is jointly computed using its strength and direction. The direction of a field point on the line is essentially the orientation of the line. Furthermore, a field point on a strong line should be distinguished from a field point on a weak line. Thus, the accumulation weights of different field points are not equal. By summing up the accumulation weights, the straight lines in the SAR image space are directly converted into several local peaks in the parameter space. A sort-window peak detection method is proposed to suppress the spurious secondary peaks in the parameter space. The experimental results show that the proposed line detection method is robust to noise and has a good antiocclusion ability. The proposed method performs well in terms of true positive detection rate and detection accuracy for both synthetic and real-world images.
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  • 103
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: A method for extracting spatiospectral features from hyperspectral (HS) data is proposed. Based on 2-D Gaussian derivative (GD) functions, a bank of 2-D filters is designed. These filters are utilized with various scale parameters to form a multiscale filter bank. This filter bank is applied on a few principal components extracted from HS data, resulting in a set of features called GD features. Four different scenarios are examined to employ these features in HS image classification. Some experiments are conducted on three well-known HS data sets. Based on the results, one scenario is adopted as the proposed framework. The effect of differentiation order of GD filters on classification accuracies is investigated. The proposed method is compared with some state-of-the-art spatiospectral methods. The experimental results confirm the remarkable performance of GD features in HS image classification.
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  • 104
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: In this letter, we investigate an application of a generalized polar decomposition of the Mueller matrix for polarimetric synthetic aperture radar (Pol-SAR) classification. Six roll-invariant parameters (diattenuation, retardance, polarization power, depolarization anisotropy, depolarization power, and transmittance) are selected as features for the classification of scattering types. Experimental results using the AIRSAR data over Flevoland show that, for most field types, the $D!!-!!R!-!!A_{Delta}!!- Delta!!-!m_{00}$ set provides the highest classification accuracy, followed by the $D!-!R!-!P_{Delta}!-!Delta!-!m_{00}$ set which also provides better accuracy than the widely used $H!-!alpha!- !(delta!-!gamma)!-!A!-!text{span}$ set. The proposed method would be valuable for Pol-SAR interpretation.
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  • 105
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: The 2007 Decadal Survey for the National Aeronautics and Space Administration recommended an Aerosol/Cloud/Ecosystems (ACE) mission with a new advanced cloud profiling radar. In this letter, we describe ACERAD, a radar design that could meet the goals for the ACE mission. ACERAD has 35 GHz (Ka-band) and 94 GHz (W-band) channels. It would scan at Ka-band and be nadir looking at W-band. The scanning antenna geometry is a classical Dragonian illuminated by a feed array; a reduced-size prototype of the antenna has been successfully fabricated and tested. ACERAD also uses a quasi-optical transmission line at W-band for low-loss routing of the signal from the transmitter to the antenna and from the antenna to the receiver. This letter briefly describes the ACERAD instrument design and test results for some of the major technologies needed for ACERAD implementation.
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  • 106
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: This letter focuses on scattering from rough sea surface covered by insoluble oil. Oil film damps the capillary wave of the rough sea surface, which leads to a smooth profile of the contaminated sea. We investigate this phenomenon using a highly efficient iterative model based on physical optics (PO) and Huygens' equivalence principle. PO is used to calculate the scattering from the oil film and underlying rough sea surface. The Huygens equivalence principle and iterative strategy are employed to evaluate the coupled scattering between the oil film and rough sea surface. The scattering of contaminated rough sea surface based on the iterative model is compared with those using the method of moments as well as computational time and memory requirements. Then, the influence of some important parameters, such as wind speed, oil thickness, incident wave frequency, and oil coverage, on scattering is investigated and discussed in detail.
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  • 107
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: Low-frequency subsurface electromagnetic measurements are important tools for characterizing natural resources and environmental wastes. Rapid simulations of low-frequency subsurface electromagnetic measurements are still a challenge because of the large computational domain and low-frequency breakdown phenomenon. We develop an effective method to simulate these low-frequency subsurface electromagnetic measurements by using the spectral element method together with a domain decomposition method (DDM). A specific mesh has been designed based on the traveling wave nature in the air and the diffusion field nature in the underground space to greatly reduce the number of unknowns. The frequency-domain version of the Riemann solver (upwind flux) is used as an effective transmission condition to simulate the interactions between neighboring subdomains in DDM. Several numerical examples demonstrate the efficiency of the proposed approach in low-frequency subsurface electromagnetics simulations.
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  • 108
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: Accurate road centerline extraction from remotely sensed images plays a significant role in road map generation and updating. In the road extraction problem, the acquisition of labeled data is time consuming and costly; thus, there are only a small amount of labeled samples in reality. In the existing centerline extraction algorithms, the thinning-based algorithms always produce small spurs that reduce the smoothness and accuracy of the road centerline; the regression-based algorithms can extract a smooth road network, but they are time consuming. To solve the aforementioned problems, we propose a novel road centerline extraction method, which is constructed based on semisupervised segmentation and multiscale filtering (MF) and multidirection nonmaximum suppression (M-NMS) (MF&M-NMS). Specifically, a semisupervised method, which explores the intrinsic structures between the labeled samples and the unlabeled ones, is introduced to obtain the segmentation result. Then, a novel MF&M-NMS-based algorithm is proposed to gain a smooth and complete road centerline network. Experimental results on a public data set demonstrate that the proposed method achieves comparable or better performances by comparing with the state-of-the-art methods. In addition, our method is nearly ten times faster than the state-of-the-art methods.
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  • 109
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: In this letter, we propose a new version of the rotation forest (RoF) method for the pixelwise classification of hyperspectral images. RoF, which is an ensemble of decision tree classifiers, uses random feature selection and data transformation techniques (i.e., principal component analysis) to improve both the accuracy of base classifiers and the diversity within the ensemble. Traditional RoF performs data transformation on the training samples of each subset. In order to further improve the performance of RoF, the data transformation is separately performed on each class, extracting sets of transformation matrices that are strictly dependent on the training samples of each single class. The approach, namely, class-separation-based RoF $(text{RoF}_{mathrm{CS}})$ , is experimentally investigated on a hyperspectral image collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. Experimental results demonstrate that the proposed methodology achieves excellent performances, in comparison with random forest and RoF classifiers.
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  • 110
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: An evolutionary algorithm (EA)-assisted spatial sampling methodology is proposed to assist decision makers in sensor network (SN) deployments. We incorporated an interpolation technique with leave-one-out cross-validation (LOOCV) to assess the representativeness of a particular SN design. For the validation of our method, we utilized Tasmania's South Esk Hydrological Model developed by the Commonwealth Scientific and Industrial Research Organisation, which includes a range of environmental variables describing the landscape. We demonstrated that our proposed methodology is capable of assisting in the initial design of SN deployment. Ordinary Kriging is shown to be the best suited spatial interpolation algorithm for the EA's LOOCV under the current empirical study.
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  • 111
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: The Kekexili region of the Tibetan Plateau has become warmer and wetter since the 1960s, resulting in a significant expansion of Zhuonai Lake (+0.46 km 2 /year, $p < 0.05$ ) before an outburst flood event occurred on September 15, 2011, and mapped by the Chinese Huanjing (HJ)-A/B satellites with a two-day revisit ability and a 360-km orbit swath. The direct cause of the outburst was due to relatively heavy precipitation from May to September 2011, specifically the continuous rainfall from later August to middle September. Two nearby earthquakes that occurred two months before the outburst might have impacted the natural structure of the lakebed and moraine dam to accelerate the outburst. The outburst event of Zhuonai Lake caused large environmental impacts on the region: 1) the desertification of the exposed lakebed of Zhuonai Lake; 2) the significant expansion of the three downstream lakes Kusai, Haidingnuoer, and Salt Lakes that not only caused the grassland reduction and deteriorations but also the potential threat to the operations of the Qing-Tibet Railway and Highway; and 3) the calving relocation of Tibetan antelopes to the shore area of Kusai Lake due to the deep cutting riverbanks caused by the overflow of Zhuonai Lake. This study provides some scientific clues or alerts for local or central governments to pay some attention on this very issue so that possible future devastative disasters and environmental damages would be avoided or mitigated.
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  • 112
    Publication Date: 2016-03-25
    Description: This letter proposes a micro-Doppler (m-D) reconstruction method for spaceborne synthetic aperture radar (SAR) images using azimuth time–frequency tracking of the phase history. The algorithm involves an azimuth defocusing of the SAR image in order to gain access to the phase history, followed by a time–frequency tracking algorithm. The tracking in azimuth is based on local polynomial phase modeling using as estimator for the polynomial coefficients the high-order ambiguity function. The approach is presented in the context of vibration estimation for infrastructure monitoring applications, with an emphasis on the estimation of vibration parameters from the reconstructed m-D. The procedure is tested and compared with state-of-the-art methods by various simulation scenarios in keeping with typical high-resolution SAR imaging parameters. Finally, the developed algorithm is applied on real data acquired by the TerraSAR-X satellite over the Puylaurent water dam in France.
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  • 113
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
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  • 114
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: Electromagnetic scattering from conducting objects is investigated for the applications of underground detection. The air–soil composite is modeled by a classical half space where the dyadic Green's function can be defined and formulated. The electric field integral equation (EFIE) is employed to guarantee the accuracy and robustness of the analysis for arbitrarily shaped scatterers. The internal resonance of EFIE is first studied under typical working conditions (frequencies, soil water contents, etc.). It is shown that this spurious resonance is much alleviated due to loss of the soil. Next, the unbounded and ill-posed spectrum of the EFIE operator is remedied by a novel localized half-space Calderón preconditioner by further exploring the lossy nature of the background. Such preconditioning is extremely useful for objects containing multiscale features. Finally, the half-space multilevel fast multipole algorithm based on real-image approximation is adopted to accelerate the computation for electrically large scatterers. Several examples in the applications of subsurface sensing are demonstrated to validate the efficiency and accuracy of this method.
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  • 115
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: A content-adaptive image reconstruction method is proposed and applied to enhance the legibility of a set of complex images. It is adapted here to visualize polarimetric synthetic aperture radar (PolSAR) images. The method is based on the joint use of all known target polarimetric features proposed in the literature. The method provides a reduction of the high-dimensional quasi-complete feature space to obtain three components as a basis for visualization. The reduction is achieved after a prior segmentation so that the most representative component can be chosen based on a local overcomplete dictionary. Finally, a color-coded image is constructed using the three most important features, which are diverse in structure. The images show that the terrain details are explored, sharper details appear, and some hidden targets are highlighted.
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  • 116
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: The GRS Society invites application for Institutional Listings from firms interested.
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  • 117
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: Building effective image representations from hyperspectral data helps to improve the performance for classification. In this letter, we develop a hierarchical discriminative feature learning algorithm for hyperspectral image classification, which is a deformation of the spatial-pyramid-matching model based on the sparse codes learned from the discriminative dictionary in each layer of a two-layer hierarchical scheme. The pooling features achieved by the proposed method are more robust and discriminative for the classification. We evaluate the proposed method on two hyperspectral data sets: Indiana Pines and Salinas scene. The results show our method possessing state-of-the-art classification accuracy.
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  • 118
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: Intercalibrating multiple radiometers using data from collocated oceanic fields of view has been a critical method for assessing in-flight instrument performance and reconciling constellation data. In this letter, we present a simple and effective improved method for radiometer intercalibration and apply it to the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the core radiometer of the GPM constellation. By optimizing the collocation criteria, this method is able to produce improved scan-dependent calibration as indicated by the ability to characterize small-scale anomalies and edge-of-scan biases within GMI observations. The accurate characterization of scan-dependent calibration also allows further examination of the intercalibration dependence on brightness temperature (TB). An effective novel method using the cumulative distribution of TBs is used to examine the impact of TB on scan-dependent calibration. It is shown that intercalibration is dependent on TB; however, the GMI magnetic anomalies are additive signals independent of TB and therefore can be corrected by simply subtracting it from the data.
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  • 119
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-03-25
    Description: Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.
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  • 120
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2015-12-29
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  • 121
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2015-12-29
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  • 122
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2015-12-29
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  • 123
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2015-12-29
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  • 124
    Publication Date: 2016-01-08
    Description: In the electromagnetic field, radar is widely used to measure or estimate the media parameters or to detect targets through obstructions. For horizontally stratified media, the layer thickness can be deduced from the time delays of backscattered echoes and the dielectric constants. The high-resolution method estimation of signal parameters via rotation invariance techniques (ESPRIT) has been proposed for time-delay estimation. In practice with a radar, backscattered echoes are correlated. In order to apply the ESPRIT method, in this letter, we propose to use two adaptive improved spatial smoothing techniques with the propagator method for fighting against the correlation between the echoes. The proposed solution does not use any approximation. Numerical examples are provided to show the performance of the algorithm.
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  • 125
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: The derivation of shallow-water bathymetry from multispectral satellite images has become a highly active field of research in recent years. Nowadays, as satellite images become more and more freely available worldwide and easily accessible, this type of technique is a cost-effective surrogate for the derivation of bathymetric information, even about the most remote areas. In fact, traditional bathymetric methods, such as acoustic and LIght Detection And Ranging (LIDAR) systems, are still very expensive and difficult to operate. Among all the models that have been presented in literature for multispectral bathymetry, the log-linear inversion model proposed by Lyzenga is still the most popular model, which is due to its simplicity and physically intuitive nature. However, it is well known that it has relatively low accuracy and that the optical uniformity assumption is unrealistic. We propose a modified Lyzenga's model that can account for spatial heterogeneity. This is particularly important when the imaged area corresponds to heterogeneous bottom types and varying water quality. The estimation of the bathymetric parameters is performed by solving an inverse problem with a Tikhonov-like regularization term. We test the proposed model with satellite Landsat 8 multispectral images and in-situ depth measurements of a shallow-water site. The results obtained indicate that the new model is more accurate, with negligible extra complexity.
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  • 126
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    Publication Date: 2016-01-08
    Description: We address the problem of unsupervised visual domain adaptation for transferring scene category models and scene attribute models from ground view images to overhead view very high-resolution (VHR) remote sensing images. We introduce a discriminative cross-view subspace alignment algorithm where each view is represented by a subspace spanned by eigenvectors. The source subspace is created using partial least squares correlation, whereas the target subspace is constructed by principal component analysis. Then, a mapping that aligns the source subspace and the target subspace is learned by minimizing a Bregman matrix divergence function. Finally, we project the labeled source data into the target aligned source subspace and the unlabeled target data into the target subspace and perform classification. Experimental results demonstrate that it is possible to use a scene category model or a scene attribute model learned on a set of ground view scenes for classification of VHR remote sensing images. Furthermore, the transferred visual attribute-based representations are human understandable and the classification results are better or comparable with state-of-the-art methods.
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  • 127
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    Publication Date: 2016-01-08
    Description: By introducing a crater-shaped window (CSW) instead of the traditional square-shaped window (SSW), an edge detector with low false positive rate is proposed to rapidly extract thin edges of synthetic aperture radar images. For further refining the true positive rate, we skillfully introduce an edge compensation strategy. Using the CSW, the square successive difference of averages is calculated. Then, edge compensation strategy is used. The CSW has low sidelobe and high localization accuracy to ensure that a detector has low false positive rate. Moreover, by using edge compensation strategy, boundaries between two similar homogeneous areas can be easily extracted. The proposed detector, hence, has high true positive rate. Both objective and subjective experiment results show that the edge detector using CSW and having edge compensation strategy attains better performance than one using SSW and without edge compensation strategy.
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  • 128
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    Publication Date: 2016-01-08
    Description: Recursive band processing of orthogonal subspace projection (RBP-OSP) is developed according to the band sequential (BSQ) format acquired by a hyperspectral imaging sensor. It can be implemented band by band recursively without waiting for data being completely collected. This is particularly important for satellite communication when data download is limited by bandwidth and transmission. Unlike band selection which requires prior knowledge of how many bands are needed to be selected, RBP-OSP has capability which allows different process units to process data whenever bands are available. In addition, it also enables users to identify significant bands during data processing. Finally and most importantly, RBP can provide progressive profiles on OSP performance, which is the best advantage that RBP-OSP can offer and cannot be accomplished by any one-shot operator.
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  • 129
    Publication Date: 2016-01-08
    Description: The performance of high-frequency skywave over-the-horizon radar is affected by transient interference. Traditional methods are used to suppress the transient interference in several steps. First, the position of the transient interference is detected in the time domain, and then the corrupted time samples are set to zeros. Then, the partial data are reconstructed by interpolation. However, the performance of the target detection would be decreased if the interference corrupts a significant portion of the received signal. In this letter, a novel approach based on robust principal component analysis (RPCA) is proposed to suppress the transient interference. Using a Hankel structured matrix constructed by the received signal in a given range cell, the transient interference excision is solved by RPCA, and a modified singular value thresholding algorithm is proposed to address the convex optimization problem. Without transient interference location and data interpolation, the proposed method can remove the transient interference, and it is also helpful for noise suppression. The experimental results show the effectiveness of the proposed method.
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  • 130
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: This letter addresses the problem of synthetic aperture radar (SAR) image recovery in the presence of radio frequency interference (RFI), which degrades SAR image quality if it is not effectively suppressed. In this letter, the RFI is modeled as the superposition of multiple complex sinusoids such that the RFI suppression problem is transformed to a frequency estimation problem. To accurately estimate the amplitudes of the sinusoids and their corresponding frequencies in the case of a low number of range samples, the frequency sparsity in the frequency domain is successfully exploited. From the estimated amplitudes and frequencies, the RFI can be reconstructed and then used for suppression. To recover the signal of interest (SOI) and by utilizing the estimated RFI, a joint estimation is derived to simultaneously perform the RFI suppression and the SOI recovery. This joint approach can effectively suppress the RFI even if it overlaps with the SOI in both the time and frequency domains. The common threshold decision approach is not required for our joint estimation to reduce the RFI. Simulation results and real-world experiments are presented to demonstrate the superior performance of the joint estimation algorithm.
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  • 131
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    Publication Date: 2016-01-08
    Description: Multisensor image fusion or pansharpening aims to sharpen a multispectral (MS) image by integrating the detail map derived from a panchromatic (Pan) image. The intensity–hue–saturation (IHS)-based methods are well adopted in pansharpening applications. However, the pansharpened MS images by IHS-based methods usually suffer from serious spectral distortions and local artifacts due to the mismatch between the estimated detail map and its ground truth. To overcome these defects, we propose a weighted least squares (WLS)-filter-based method in this letter. Different from existing IHS-based methods, the proposed method eliminates the influence of the low-frequency components of the Pan and MS images with the WLS filter. Moreover, the derived detail map is further refined based on the spectral signatures for different bands of the MS image. We test the proposed method on various satellites data; the experimental results demonstrate that the proposed method performs well in both spectral and spatial qualities.
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  • 132
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: This letter presents a cost-effective technique for through-the-wall imaging of objects beyond a wall. The approach relies on an amplitude-only multimonostatic radar that operates as a synthetic aperture radar. In contrast to conventional approaches, the system employs recent broadband techniques for phase retrieval. Thus, the complexity of the scanner is reduced, whereas it preserves the capacities of a conventional broadband system to retrieve the 3-D profile of objects. Moreover, the system is compatible with state-of-the art techniques that require full (i.e., amplitude and phase) acquisitions. Results at different frequency bands are shown to illustrate how the system can provide accurate estimation of the profile of metallic objects behind building materials such as plywood, plasterboard, or hollow bricks and mortar.
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  • 133
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: We present a novel marine oil spill segmentation method that characterizes two smoothing modules at the label level and the pixel level separately. At the label level, we exploit the rolling guidance filter for smoothing the label cost volumes. It enables scale-aware labeling and thus alleviates the ambiguous segmentation that blurs the detailed structures of oil spills. At the pixel level, we adapt a cooperative model for smoothing higher order pixel variations, which has the potential of preserving elongated strips that often arise in oil spills. We integrate the two smoothing modules operating at different levels into an energy minimization formulation, which is referred to as dual smoothing. The coupling of the two smoothing modules enables an effective complement to each other such that the specific structures of oil spills are accurately characterized. We compute the optimal labeling of the dual-smoothing framework based on graph cuts. The proposed dual-smoothing framework is especially effective in segmenting elongated and detailed oil spills, and the experimental results demonstrate its advantages over thresholding- and graph-cut-based segmentations.
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  • 134
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: In this letter, several important aspects are addressed for highly squinted synthetic aperture radar data with subaperture (SA) processing. First, the influences of the reference linear range walk correction and the resulting residual Doppler centroid are addressed. Then, the differences between the full-aperture data and SA data in the azimuth focusing are investigated in detail. Finally, a novel frequency-nonlinear-chirp-scaling algorithm with an addition of highly varying residual Doppler centroid correction over azimuth is proposed to equalize the azimuth-variant Doppler parameters so that the uniform azimuth processing can be realized. Compared with the previous works, this addition greatly improves the focused quality of the final image. Airborne real data processing validates the effectiveness of the proposed algorithm.
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  • 135
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: Time–frequency analysis is able to reveal the useful information hidden in the seismic data. The high resolution of the time–frequency representation is of great importance to depict geological structures. In this letter, we propose a novel seismic time–frequency analysis approach using the newly developed empirical wavelet transform (EWT). It is the first time that EWT is applied in analyzing multichannel seismic data for the purpose of seismic exploration. EWT is a fully adaptive signal-analysis approach, which is similar to the empirical mode decomposition but has a consolidated mathematical background. EWT first estimates the frequency components presented in the seismic signal, then computes the boundaries, and extracts oscillatory components based on the boundaries computed. Synthetic, 2-D, and 3-D real seismic data are used to comprehensively demonstrate the effectiveness of the proposed seismic time–frequency analysis approach. Results show that the EWT can provide a much higher resolution than the traditional continuous wavelet transform and offers the potential in precisely highlighting geological and stratigraphic information.
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  • 136
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: In this letter, we propose a new methodology to estimate the absolute digital elevation model (DEM) of an area by radargammetric-like processing of interferometric multibaseline synthetic aperture radar (SAR) data from two opposite-side surveys. Two DEMs of the imaged area obtained from two opposite-side tomographic SAR views are coregistered, correcting residual baseline errors. This methodology combines the great accuracy of multibaseline interferometric processing with precise stereo plotting typical of opposite-side radargrammetry, requires no texture matching and no control points, and is applicable also in the case of few a priori information about the site topography.
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  • 137
    Publication Date: 2016-01-08
    Description: We propose the use of deep convolutional neural networks (DCNNs) for human detection and activity classification based on Doppler radar. Previously, proposed schemes for these problems remained in the conventional supervised learning paradigm that relies on the design of handcrafted features. Whereas these schemes attained high accuracy, the requirement for domain knowledge of each problem limits the scalability of the proposed schemes. In this letter, we present an alternative deep learning approach. We apply the DCNN, one of the most successful deep learning algorithms, directly to a raw micro-Doppler spectrogram for both human detection and activity classification problem. The DCNN can jointly learn the necessary features and classification boundaries using the measured data without employing any explicit features on the micro-Doppler signals. We show that the DCNN can achieve accuracy results of 97.6% for human detection and 90.9% for human activity classification.
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  • 138
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: In this letter, a novel hierarchical sparse representation-based classification (HSRC) for synthetic aperture radar (SAR) images is proposed. Features utilized in HSRC are extracted from the multisize patches around each pixel to precisely describe the complex terrains. Two thresholds are introduced in the sparse representation classifier to restrict the range of reconstruction residual, which classifies the reliable classified points, and the rest of the pixels are considered as the uncertain ones in the original SAR image. Then, a new dictionary is constructed by the reliable pixels, and the uncertain pixels will be reclassified in the next classification layer. The hierarchical structure is very reasonable and effective to employ simple features in each layer for describing the various topographic types. Compared with traditional sparse representation-based classification and support vector machines in several fixed-size patches, the proposed method can obtain better performance both in quantitative evaluation and visualization results.
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  • 139
    Publication Date: 2016-01-08
    Description: In this letter, we proposed a phase analysis of the 3-D Zernike moment (3D-ZM), to estimate the orientation difference between buildings in a digital surface model (DSM). A 3-D analysis using the DSM is an important way for building reconstruction and many further remote sensing applications. By using the 3D-ZM, we could decompose the 3-D structure of an object in a complex domain. Benefiting from rotation invariance of the amplitude component, the 3D-ZM has excellent performance for object classification. However, the phase component of 3D-ZM is ignored in early research, by which orientation of different objects could be analyzed, and similar buildings (within one class) could be distinguished meticulously, whereas other traditional geometric features may fail to do so. Therefore, we studied the phase analysis of 3D-ZM and introduced a flow frame of orientation-difference estimation. Experimental results illustrated that our method could robustly find the orientation difference between similar buildings, and improvement on accuracy was achieved for building classification and orientation.
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  • 140
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: Measurements of the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission–Water 1 (GCOM-W1) satellite at 6.925 and 7.3 GHz and both linear polarizations over tropical cyclones (TCs) during 2012–2014 are used to derive a new geophysical function relating the brightness temperature to the sea surface wind speed (SWS) in extreme conditions. Similar sensitivity to the SWS at close C-band frequencies allowed correcting for the atmospheric contributions to the microwave radiance and estimating the brightness temperature $(T_{B})$ at the surface under TCs, combining theoretical modeling and measured $T_{B}$ analyses. Estimated oceanic $T_{B}$ 's were regressed against the wind speeds from the Best Track Archive to derive a new geophysical model function for the wind speed excess emissivity at AMSR2 C-band microwave frequencies.
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  • 141
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: An improved firefly algorithm (FA)-based band selection method is proposed for hyperspectral dimensionality reduction (DR). In this letter, DR is formulated as an optimization problem that searches a small number of bands from a hyperspectral data set, and a feature subset search algorithm using the FA is developed. To avoid employing an actual classifier within the band searching process to greatly reduce computational cost, criterion functions that can gauge class separability are preferred; specifically, the minimum estimated abundance covariance and Jeffreys–Matusita distances are employed. The proposed band selection technique is compared with an FA-based method that actually employs a classifier, the well-known sequential forward selection, and particle swarm optimization algorithms. Experimental results show that the proposed algorithm outperforms others, providing an effective option for DR.
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  • 142
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate classification results when provided with large enough data sets and respective labels. However, using CNNs along with limited labeled data can be problematic, as this leads to extensive overfitting. In this letter, we propose a novel method by considering a pretrained CNN designed for tackling an entirely different classification problem, namely, the ImageNet challenge, and exploit it to extract an initial set of representations. The derived representations are then transferred into a supervised CNN classifier, along with their class labels, effectively training the system. Through this two-stage framework, we successfully deal with the limited-data problem in an end-to-end processing scheme. Comparative results over the UC Merced Land Use benchmark prove that our method significantly outperforms the previously best stated results, improving the overall accuracy from 83.1% up to 92.4%. Apart from statistical improvements, our method introduces a novel feature fusion algorithm that effectively tackles the large data dimensionality by using a simple and computationally efficient approach.
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  • 143
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-01-08
    Description: In this letter, an imaging method with array factor synthesis in synthetic aperture interferometric radiometers (SAIRs) is presented. The presented method can efficiently control the characteristics of the equivalent array factor of SAIR, such as null position and depth, sidelobe level, and so on, which will be beneficial in the presence of potential interferences, such as radio frequency interference. The method is based on the formulation that the SAIR array is equivalent to a phased array with a virtual antenna element located in each baseline. The previous built-in method and the conventional Fourier inverse method can be seen as a special case of the presented one. Numerical results validate the effectiveness of the presented method.
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  • 144
    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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  • 145
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 146
    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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  • 147
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 148
    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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  • 149
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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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  • 150
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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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  • 151
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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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  • 152
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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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  • 153
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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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  • 154
    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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  • 155
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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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  • 156
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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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  • 157
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 158
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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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  • 159
    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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  • 160
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 161
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 162
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 163
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 164
    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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  • 165
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 166
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 167
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 168
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 169
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    Institute of Electrical and Electronics Engineers (IEEE)
    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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  • 170
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: Forward velocity extraction is a very important process for obtaining a high-quality unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) image. Because of the constraints of low flying altitude and small platform size, the flight path of the UAV is easily disturbed by the atmospheric turbulence. The complex motion error of the UAV's flight path makes the forward velocity difficult to be extracted from raw SAR data. To address this problem, an adaptive notch filtering (ANF)-based approach for forward velocity extraction is proposed. Based on the kinetic characteristics of the UAV, the variation of Doppler centroid frequency is analyzed and exploited to remove most components of the cross-track acceleration in the low-frequency range. Then, by regarding the forward velocity component as a narrow-band component, ANF processing is employed to extract it from the estimated Doppler rate. Comparing with the methods reported in the literature, the ANF method can achieve higher accuracy and efficiency due to its excellent notching performance and strong suppression for narrow-band signals. Promising results from raw data experiments are presented to demonstrate the validity and superiority of the proposed method.
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  • 171
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    Publication Date: 2016-08-09
    Description: The Geostationary Interferometric Microwave Sounder (GIMS) is a new concept of atmospheric microwave sounder for China's future geostationary Earth orbit meteorological satellite (FY-4). A novel self-calibration method for interferometric radiometers with a rotating thinned array, particularly for GIMS, has been proposed in this letter. Compared with the traditional relative phase calibration approach, neither dedicated hardware nor dedicated calibration working model is needed to achieve the relative phase calibration in this novel interelement phase calibration. The self-calibration approach is inherently merged with the nominal observation working model of GIMS, owing to the continuous array rotating of the GIMS instrument. A running average scheme has been introduced into the self-calibration approach to enhance the signal-to-noise ratio of the calibration data, which is normally very low with the natural earth scene. The method is demonstrated by both simulation and field imaging experiment.
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  • 172
    Publication Date: 2016-08-09
    Description: This letter presents an alternative approach for investigating the teleconnections of summer monsoon rainfall in the state of Kerala, India, with four prominent climatic oscillations in multiple time scales based on multivariate empirical mode decomposition (EMD) (MEMD) and time-dependent intrinsic correlation (TDIC). First, the multivariate data set constituting summer monsoon rainfall time series and the climate oscillations such as the Quasi-Biennial Oscillation, El Niño Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation, and Equatorial Indian Ocean Oscillation are decomposed to several rotational modes by employing MEMD. To capture the associations in shorter time spells, the TDIC method is employed, in which the sliding window is adaptively fixed based on instantaneous periods obtained by the Hilbert transform of the obtained modes. From the analysis, it is found that the strength and nature of association between summer monsoon rainfall and the climate oscillations vary with time scales and time spells. The performance of MEMD-based TDIC analysis is compared with that of EMD-based TDIC, and it was found that the former one well captured the direct correlation between ENSO and summer monsoon rainfall in the 1997–1998 period and the opposing correlation in the 2002–2003 period, while the latter approach failed to capture such associations properly.
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  • 173
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    Publication Date: 2016-08-09
    Description: Radar imaging is typically based on linear models of the electromagnetic scattering phenomenon. These models are robust and computationally efficient, but do not account for mutual interactions among targets in the scene and between the targets and the surrounding environment. As a result, the radar images are characterized by spurious targets, i.e., multipath ghosts, which appear at positions where no physical targets exist. In this letter, we compare two key approaches for clutter suppression. The first approach applies multiplicative fusion of the images corresponding to subapertures of the deployed array, whereas the second approach is based on coherence factor filtering, which enhances the image quality by suppressing low-coherence features. We assess the performance of these two methods in terms of imaging and detection capabilities. Numerical results based on synthetic data are reported to support the comparative analysis.
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  • 174
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: Even after coregistration, very high resolution (VHR) multitemporal images acquired by different multispectral sensors (e.g., QuickBird and WordView) show a residual misregistration due to dissimilarities in acquisition conditions and in sensor properties. Residual misregistration can be considered as a source of noise and is referred to as registration noise (RN). Since RN is likely to have a negative impact on multitemporal information extraction, detecting and reducing it can increase multitemporal image processing accuracy. In this letter, we propose an approach to identify RN between VHR multitemporal and multisensor images. Under the assumption that dominant RN mainly exists along boundaries of objects, we propose to use edge information in high frequency regions to estimate it. This choice makes RN detection less dependent on radiometric differences and thus more effective in VHR multisensor image processing. In order to validate the effectiveness of the proposed approach, multitemporal multisensor data sets are built including QuickBird and WorldView VHR images. Both qualitative and quantitative assessments demonstrate the effectiveness of the proposed RN identification approach compared to the state-of-the-art one.
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  • 175
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    Publication Date: 2016-08-09
    Description: Recently, collaborative representation for hyperspectral image analysis has received great interest. Due to the effectiveness of local manifold in a tangent space, this letter extends the collaborative representation classification (CRC) mechanism into the tangent space. Specifically, this letter uses simplified tangent distance and a new regularization term and designs a modified classifier innovatively. Moreover, two variants with weighted diagonal matrices to adaptively adjust the regularization terms are developed to further improve the classification performance. In the experiments, two real hyperspectral images were adopted for performance evaluation, and the experimental results demonstrate that the proposed algorithms can significantly improve classification results compared with the original CRC algorithm and other related classifiers.
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  • 176
    Publication Date: 2016-08-09
    Description: When designing a high-precision airborne radar altimeter, the approach of an analysis of the characteristics of radio signals reflected from a statistically rough sea surface based on its steady-state model becomes invalid. In the measurement of the sea surface profile, a characteristic of the reflection properties is the local backscattering pattern of the radar cross section, which depends on the time and the relative position of the low-altitude carrying vehicle from the surface. The effective width of the local backscattering pattern is determined by the statistical characteristics both of the small-scale roughness and the large-scale roughness of the surface. The location of the maximum value of the local backscattering pattern depends on the derivative of the large-scale roughness of the sea surface, i.e., the shifts in proportion to the slopes of the large-scale surface roughness. These effects must be considered when analyzing the measurement errors of a low-altitude radar altimeter.
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  • 177
    Publication Date: 2016-08-09
    Description: When combined with digital beam-forming (DBF) techniques, multichannel synthetic aperture radar (SAR) systems can achieve high-resolution and wide-swath SAR imaging. However, inevitable channel biases will degrade the performance of DBF in practice. To address this problem, a novel channel phase bias estimation algorithm is proposed in this letter. Theoretical analysis reveals that the signal of the first channel, which is considered as the reference channel, can be reconstructed from the signals of other channels, regardless of noise and signals outside the Doppler bandwidth. In the presence of phase biases, there is a reconstruction error after the cancellation by subtracting this reconstructed signal from the original signal. However, by minimizing the reconstruction error, the channel phase biases can be precisely estimated. The effectiveness of the proposed algorithm is validated by the experimental results.
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  • 178
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    Publication Date: 2016-08-09
    Description: A backprojection algorithm (BPA), a time domain synthetic aperture radar (SAR) imaging algorithm, can be widely used without modification in various imaging modes, like stripmap, spotlight, sliding spotlight, etc. However, BPA also suffers when used in azimuth multichannel SAR due to the nonuniform sampling problem caused by a nonideal pulse repetition frequency. In this letter, we propose a weighted BPA (WBPA) for azimuth multichannel SAR imaging. Derived from the filter-bank-based reconstruction method, WBPA adds a weighting procedure to BPA. Simulations and an airborne SAR data experiment demonstrate that WBPA can perfectly suppress the azimuth ambiguities under band-limited circumstances. WBPA extends the applicable scope of BPA.
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  • 179
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    Publication Date: 2016-08-09
    Description: A reliable estimate of emissivity is critical for a wide range of applications for the atmosphere, the biosphere, the lithosphere, the cryosphere, and the hydrosphere. This study uses three years (August 2012 to July 2015) of data from the Advanced Microwave Scanning Radiometer-2 sensor that is onboard the Global Change Observation Mission 1st Water satellite to explore estimates of instantaneous global land emissivity. A method is adopted to remove the known inconsistency in penetration depths between microwave brightness temperatures and infrared-based ancillary data that could cause differences between day and night emissivity estimates. After removing the diurnal atmospheric effects, the resulting retrieved cloud-free land emissivities realistically represent well-known large-scale features. As expected, the polarization differences of estimated emissivities show noticeable seasonal variations over the deciduous woodland and grassland regions due to changes in vegetation density. The potential of estimated emissivities for high-latitude snow detection and freeze/thaw state identification is also demonstrated.
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  • 180
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    Publication Date: 2016-08-09
    Description: While data-dependent dimensionality reduction has dominated in many applications of hyperspectral imagery, there is increasing interest in data-independent strategies—such as random projections—due to their promise for reduced computational complexity as well as their demonstrated ability to preserve application-important information. Such random-projection-based dimensionality reduction is investigated in the specific context of supervised hyperspectral classification. Both Hadamard- and Gaussian-based random projections are considered, applied alone as well as incorporated into a fast approximate singular value decomposition (SVD). Experimental results reveal that the proposed Hadamard-based random projection with the fast SVD (FSVD) offers a computationally attractive alternative to not only traditional SVD but also Gaussian-based FSVD for dimensionality reduction in hyperspectral classification.
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  • 181
    Publication Date: 2016-08-09
    Description: Use of Global Navigation Satellite Systems (GNSS) receivers for real-time applications has improved significantly all over the world. The main problem with the designed receivers is their failure to function under harsh environmental conditions because of the structured phase-locked loop (PLL) architecture. One of the most critical phenomena that cause signal degradation in GNSS receiver is ionospheric scintillations, which create disturbances in amplitude and phase of the received signal. The problem in signal acquisition and tracking, even in the severe canonical fades (deep amplitude fading correlated with reference to half cycle phase jumps), can be mitigated using robust and adaptive carrier tracking algorithms. The autoregressive exogenous modeling parameters are useful in estimating the amplitude and phase scintillations. The proposed adaptive-extended Kalman filter (AEKF) approach works as an effective carrier tracking algorithm maintaining a balance in dual problems faced by PLL-based receivers, i.e., (estimation versus mitigation) and (dynamics versus noise reduction) tradeoff. The developed AEKF algorithm performed well for synthetic Cornell scintillation monitor data and for Global Positioning System L1 PRN 12 data collected around 21.30 H (local time) on October 24, 2012, in Rio de Janeiro, Brazil, with GNSS Software Navigation Receiver.
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  • 182
    Publication Date: 2016-08-09
    Description: The back-projection (BP) algorithm is an ideal solution for large-swath airborne SAR imaging. However, space-variant phase errors induced by trajectory deviations dramatically degrade the BP-focusing performance in a large-swath mode. In this letter, we propose an autofocus method that is compatible with the BP imagery, in which the phase-error function is constructed for individual pixels. In the new method, multiple local areas at different illumination directions within the radar beam are synthesized to trace the local phase gradient. From these phase gradient estimates, the accurate pixel-wise phase-error correction for all the contaminated pulses is achievable. This approach is capable of correcting space-variant phase errors with high precision and efficiency for large-swath SAR imaging. Experiments based on the real data that are recorded by a highly squinted SAR system validates the effectiveness of the proposed autofocus method.
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  • 183
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    Publication Date: 2016-08-09
    Description: The behavior of the Ka-band backscattering coefficient at nadir and close-to-nadir angles for land applications is poorly documented. The measurements made during a ground-based campaign at Ka-band were performed at nadir and close-to-nadir angles over bare soils for different surface roughness and soil moisture conditions. The resulting backscattering levels exhibited a dynamic range of approximately 23 dB at nadir for soil moisture contents between 5% and 50% m 3 /m 3 over both smooth and rough surfaces. These results were then compared to the geometric optics (GO) and millimeter microwave (MMW) models. Generally, GO finely fits the backscattering coefficients close to nadir, and MMW appeared to fit for larger incidence angles or rough surfaces. The results obtained in this letter can address prelaunch science and engineering considerations for the interferometry–altimetry Surface Water and Ocean Topography mission operating at Ka-band.
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  • 184
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: Sidelobe reduction is important to improve the quality of through-the-wall radar imagery, and adaptive sidelobe reduction (ASR) is the widely used method for that. However, similar to coherent factor (CF)-based methods, the major defect of ASR is to suppress the weak targets in the imaging scene. Moreover, in near-field radar applications, including through-the-wall imaging, ASR cannot achieve the global optimum. In most letters about ASR, the three crucial parameters, the filter order, the interpolation factor, and the constraint threshold, are selected empirically. This letter analyzes their relationships and value ranges and then proposes a modified ASR (M-ASR) method, which has a more reasonable strategy of parameter assignment. Both simulative and experimental results validate the feasibility of M-ASR in the near-field application and its improvement in weak target detection.
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  • 185
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: This letter aims to show the potential of using polarimetric parameters to distinguish between large birds and unmanned aerial vehicles (UAVs) of comparable size in the context of a modern long-range air defense radar. Time is a critical resource in such systems, and techniques for robust noncooperative target recognition not relying on spatial resolution or long dwell times are highly desired. Furthermore, methods less dependent on target micromotion are, in many cases, required. Methods exploiting polarimetric features are shown to have potential in both cases. An experiment in S-band shows that a simple nearest-neighbor classifier can achieve good separation between UAVs and birds both with and without detectable micromotion based on a set of polarimetric parameters alone.
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  • 186
    Publication Date: 2016-08-09
    Description: Global colocalized ocean surface measurements using the Global Precipitation Measurement near-nadir dual-frequency Ku- and Ka-band microwave measurements are analyzed and compared. Focusing on the Ka and Ku cross-sections fall-off with incidence angles, the contemporaneous measurements enable to more precisely document differing ocean scattering characteristics for both microwave frequencies. Sensitivity with wind speed and significant wave height is further reported using global comparisons with numerical estimates. As demonstrated, the bifrequency capability can provide direct means to efficiently separate short-scale wave contributions, between mean squared slope and curvature characteristics, and to further gain valuable insights concerning near-nadir instruments onboard future ocean satellite missions including the China–France Oceanography Satellite and the Surface Water Ocean Topography Mission.
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  • 187
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    Unknown
    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: Uniformly distributed feature extraction and availability of a sufficient number of correctly matched points between the input images are the key challenges for remote sensing optical image registration. Because of its robustness and distinctiveness, the scale-invariant feature transform (SIFT) is a well-known approach for an automatic image registration. However, the features obtained from the SIFT algorithm are not uniformly distributed, and sometimes, the number of matched features is insufficient to provide subpixel accuracy in the registration of remote sensing optical images. In this letter, a modified version of SIFT is proposed to obtain uniformly distributed matched features. Then, the bivariate histogram and the random sample consensus have been used to refine the initial matches. Finally, a reliable matching criterion is proposed to maximize the number of matches.
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  • 188
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: Due to the limitation of hyperspectral sensors and optical imaging systems, there are several irreconcilable conflicts between high spatial resolution and high spectral resolution of hyperspectral images (HSIs). Therefore, HSI super-resolution (SR) is regarded as an important preprocessing task for subsequent applications. In this letter, we use sparse representation to analyze the spectral and spatial feature of HSIs. Considering the sparse characteristic of spectral unmixing and high pattern repeatability of spatial–spectral blocks, we proposed a novel HSI SR framework utilizing spectral mixture analysis and spatial–spectral group sparsity. By simultaneously combining the sparsity and the nonlocal self-similarity of the images in the spatial and spectral domains, the method not only maintains the spectral consistency but also produces plenty of image details. Experiments on three hyperspectral data sets confirm that the proposed method is robust to noise and achieves better results than traditional methods.
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  • 189
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: This letter presents a suite of analysis techniques designed to support the interpretation of data from the Shallow Radar on Mars Reconnaissance Orbiter. Cross-track surface echoes (“clutter”) with a time delay similar to potential subsurface echoes are known to present a major challenge to orbital radar sounding; furthermore, we demonstrate that the broad beam pattern of orbital sounders combined with cross-track long-wavelength surface slopes can result in first-received echoes arising from locations significantly distant from the nadir point, further hindering interpretation. Combining known surface topography (of lower resolution than the radar wavelength) with an appropriate radar model and orbital parameters, we can simultaneously address both of these challenges. Our technique has been applied to a variety of scientific targets on Mars with success and is recommended as a standard practice.
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  • 190
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: Clutter suppression and ground moving target indication (GMTI) are challenging tasks for multichannel synthetic aperture radar (MC-SAR) systems. In recent years, robust principal component analysis (RPCA), such as the augmented Lagrange multiplier method (ALM) and go decomposition (GoDec) algorithm, has drawn considerable attention for its excellent performance in distinguishing the different parts from a set of correlative database. In this letter, an efficient along-track interferometry GoDec (ATI-GoDec) approach is proposed for GMTI in MC-SAR systems under a strong clutter background. The proposed method can be separated into two sections: the predetection and the postdetection. The predetection by an efficient ATI RPCA method can decrease missing targets, and postdetection with a novel magnitude and phase (M&P) detection has the ability to reduce the false targets. As a result, the proposed method can provide a more robust performance by a comparison to the traditional ATI detection. It can also widen the tolerant values of the preset cardinality and decrease the probability of false alarm compared with the conventional GoDec algorithm. Moreover, the proposed method only takes several iterations to reach the convergence by solving the optimization problem of the RPCA model, which makes it more efficient than the previous RPCA methods. The results by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this letter.
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  • 191
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: This letter proposes a novel algorithm for reliable and accurate ground control point (GCP) estimation, which uses vector road network data as a reference. Roads are detected in satellite images, and road patches are used as a template, which is matched against the vector road data by using a nonlinear criterion based on the distance transform. The proposed approach is compared with the standard correlation-based template matching in terms of the reliability and accuracy of localization on a test set of five RapidEye images with 380 reference control points. The experiments demonstrate superior reliability with respect to the size of the search window and the variable image acquisition conditions. While the subpixel accuracy is achieved by the cross-correlation method over the search area up to 20% of the template size, the proposed method achieves comparable accuracy over a search area that is ten times larger than the template image.
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  • 192
    Publication Date: 2016-08-09
    Description: This letter deals with a recently proposed two-step algorithm for the reconstruction of dielectric and velocity profiles of axially moving cylinders. In particular, the aim of the present work is to investigate the limitations and capabilities in reconstructing the velocity profile. By considering a best-case scenario, it is shown that, although the algorithm should be able to work even in the presence of flows with very small axial velocity values, reliable results can only be obtained when the axial speed is larger than a lower bound. This bound is shown to be an intrinsic limitation of the algorithm which is only related to the cross-polarization isolation of the measurement system. Other simulations, based on worst-case scenarios, confirm that such a bound is independent of the type of flow considered, of the configuration of the measurement system, and of the errors coming from the step devoted to the reconstruction of the dielectric profile. Finally, the same simulations show that the algorithm is reliable in a wide range of speed values, provided sensors with large cross-polarization isolations are exploited.
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  • 193
    Publication Date: 2016-08-09
    Description: This letter presents an algorithm for retrieving soil moisture using only H-polarized multiangular brightness temperature at L-band. We developed a parameterized surface model based on a simple-empirical model, the $H_{p}$ model, for this retrieval algorithm. By analyzing a simulated database using the advanced integral equation model (AIEM), it was found that the roughness variable $H_{h}$ can be parameterized as a function of an effective roughness parameter $Sr=(k_{L}cdot s)^{2-N}(s/l)^{N}$ . Influences of three surface roughness parameters (e.g., rms height, correlation length, and type of autocorrelation function) required to describe a rough surface on surface reflectivity were all considered in this parameterized model. Comparison with AIEM simulations over a wide range of soil conditions indicates a good performance of this model. Then, based on the $omega-tau$ model, this algorithm is applied on refined SMOS H-polarized multiangular brightness temperature. Retrieved soil moisture in Africa exhibits reasonable patterns and temporal changes. Validation using in situ soil moisture from Little Washita watershed and Yanco over 2010–2011 showed fine accuracy with root-mean-square errors of 0.031 and 0.045 m 3 /m 3 for two areas, respectively.
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  • 194
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: In this letter, we derive the distribution characteristics of first-order multipath ghosts in a nested multiple-input–multiple-output (MIMO) through-wall radar and evaluate the efficacy of the phase coherence factor (PCF) in ghost suppression. Different from a synthetic aperture radar, the first-order multipath echoes of a nested MIMO through-wall radar generate several ghosts. For example, for a nested MIMO array composed of a compact receiving subarray and $M$ spatially dispersed transmitters, there are $M$ ghosts at the same side of the wall as the array. The $m$ th ghost is supposed to occur near the intersection of the line, connecting the target and the center of the receiving subarray, and the ellipse whose foci are the positions of the target and the $m$ th transmitter. Under the assumption of phase uniform distribution clutter, the PCF can suppress the ghosts up to $-20 lg(1-sqrt {(M^{2}-1)/M^{2}})$ dB, which is about 17.46 dB when $M = 2$ .
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  • 195
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: Several spectral unmixing techniques using multiple endmembers for each class have been developed. Although they can address within-class spectral variability, their unmixing results may have low unmixing resolution when the within-class variation is large due to the associated high uncertainty. Therefore, it is critical to represent data in an effective feature space so that the endmember classes are compact with small variation. In this letter, a minimum-class-variance support vector machine (MCVSVM) is further developed to extend its functions for both classification and spectral unmixing. Moreover, analytical expressions for spectral unmixing resolution (SUR) are provided to measure the spectral unmixing uncertainty in the new feature space. The extended MCVSVM (e_MCVSVM) can improve SUR and reduce the spectral unmixing uncertainty as it can effectively maximize the between-class scatter while minimizing the within-class scatter. Experimental results show that the e_MCVSVM algorithm performs better in terms of the unmixing accuracy and the computation speed compared with the other algorithms (e.g., fully constrained least squares and endmember bundles) in both linearly separable and nonseparable cases. This newly proposed approach advances the linear spectral mixture analysis with greater speed and higher accuracy based on the SVM after the SUR is effectively characterized.
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  • 196
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: Well log peak shifts mean that the peak or valley of different well logs cannot be aligned. This is a common phenomenon in thin beds. To understand the cause of well log peak shifts, a discrete convolution model is proposed to simulate the response of well logs. At first, the responses of natural gamma ray logs, acoustic logs, neutron logs, density logs, and induction resistivity logs in single thick bed, single thin bed, and complex thin beds are simulated based on the proposed model. Simulation results show that well log peak shifts did not occur in single thick bed but did occur in single thin bed and complex thin beds. Well log peak shifts were especially obvious in complex thin beds. Well log peak shifts are mainly caused by a mismatch in the vertical resolutions of different well logs and rapid physical property changes of thin beds. Finally, five basic log peak shift patterns are proposed.
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  • 197
    Publication Date: 2016-08-09
    Description: This letter is aimed to compare the performance of canopy height models (CHMs) derived from airborne laser scanning (ALS) data and unmanned aerial vehicle (UAV) stereo images in the extraction of individual tree height and crown size. Treetops were identified using the local maximum algorithm from the Gaussian filtered CHMs. A parabola fitting was used to determine the crown size. Factors affecting the delineation results, such as point cloud density and the spatial distribution and growing status of trees, were analyzed. The results showed that the UAV stereo images, together with the ALS-derived digital elevation model (DEM), can achieve better performance than ALS data alone based on our data set. The match ratio between delineated and field-measured trees varied significantly, with the highest ratio of 66.94% obtained by UAV in the young aspen forest and the lowest ratio of 33.76% obtained by ALS in the old forest. Aside from the influence of point density, this letter also shed light on the important role that the spatial distribution and growing status of trees play in the delineation of individual trees. To conclude, integrating UAV stereo images with the ALS-derived DEM is effective in delineating individual tree attributes in small-scale windbreaks, which provides some suggestions for the future management of agriculture land.
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  • 198
    Publication Date: 2016-08-09
    Description: Micro-Doppler radar signatures are capable of characterizing rich motion information of targets and have played important roles in target identification and recognition. In this letter, we develop a novel parametric time–frequency method to analyze the micro-Doppler signatures of rigid-body targets, which is referred to as the block-sparse forward–backward time-varying autoregressive (BS-FBTVAR) model. First, the basis expansion method is employed to convert the time-varying model parameter estimation problem to be time invariant. Then, by investigating the intrinsic relationship between the model parameters and the poles of rigid-body targets, block-sparsity constraints are introduced to the conventional FBTVAR model. A complex-valued block-sparse Bayesian learning algorithm is developed as the solver of the novel BS-FBTVAR model. Finally, experiments on the electromagnetic (EM) analysis data are carried out to validate the performance of the proposed method.
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  • 199
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: Surface Water and Ocean Topography (SWOT) is a future wide-swath radar altimetry mission. The main instrument is the Ka-band radar interferometer (KaRIn). It provides interferometric radar images that will be processed to obtain water elevation estimates worldwide. Due to the specific geometric and radiometric characteristics of KaRIn/SWOT, the phase-to-height conversion is one of the most critical processing steps. We propose methods to estimate water heights without using classical approaches based on spatial phase unwrapping and control points. Results obtained on the simulated SWOT data are shown.
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  • 200
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-08-09
    Description: Terrain classification is an important topic in polarimetric synthetic aperture radar (PolSAR) image processing. Among various classification techniques, the stacked sparse autoencoder (SSAE) is a kind of deep learning method that can automatically learn useful features layer by layer in an unsupervised manner. However, the scattering measurements of individual pixels in PolSAR images are affected by the speckle; hence, the performance of pixel-based classification approaches would be poor. In this situation, a novel framework is proposed to learn robust features of PolSAR data. The local spatial information is introduced into SSAE to learn the deep spatial sparse features automatically for the first time. Furthermore, the influences of the neighbor pixels on the central pixel are controlled depending on the spatial distances from the neighbor pixels to the central pixel. Experimental results with fully PolSAR data indicate that the proposed method provides a competitive solution.
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