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In this article, we propose a new line segment detection framework based on a novel pixel orientation estimation (POE) method, which detects line segments from binary edge maps and can be combined with any edge detectors. We show its efficiency by testing it in SAR images. The proposed POE-based line segment detection framework aims to leverage the success of deep learning models for edge detection in 1-look SAR images. The novel POE method estimates the orientation of each edge pixel by counting the number of edge pixels along a set of orientations. As the most edge pixels exist along the orientation of the line segment, the orientation of the edge pixel is given by the orientation that gives the maximum number of counts. Counting the number of edge pixels along different orientations is equivalent to convolving the local neighbourhood of each edge pixel with a set of carefully designed window functions with each window function corresponding to a fixed orientation. With the estimated orientations of pixels in the edge map, pixels can be grouped with a region growing step to form line support regions. Regions with their size larger than a size threshold will be accepted. Finally, rectangles are used to approximate the accepted regions and those rectangles are detected line segments. Experiments in both simulated SAR dataset and real SAR images demonstrate the efficiency of the proposed method. In particular, we advance the state-of-the-art performances by 18% (F1-score) on the 1-look dataset simulated from YorkUrban-LineSegment Dataset. [ABSTRACT FROM AUTHOR]

Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost of acquisition equipment, thereby providing a feasible way to improve the quality of remote sensing images. Clearly, image SR is a severe ill-posed problem. With the development of deep learning, the powerful fitting ability of deep neural networks has solved this problem to some extent. Since the texture information of various remote sensing images are totally different from each other, in this article, we proposed a network based on generative adversarial network (GAN) to achieve high-resolution remote sensing images, named multiattention GAN (MA-GAN). The main body of the generator in MA-GAN contains three blocks: pyramid convolutional residual dense (PCRD) block, attention-based upsampling (AUP) block, and attention-based fusion (AF) block. Specifically, the developed attention pyramid convolutional (AttPConv) operator in the PCRD block combines multiscale convolution and channel attention (CA) to automatically learn and adjust the scale of residuals for better representation. The established AUP block uses pixel attention (PA) to perform arbitrary scales of upsampling. The AF block uses branch attention (BA) to integrate upsampled low-resolution images with high-level features. Besides, the loss function takes both adversarial loss and feature loss into consideration to guide the learning procedure of the generator. We have compared our MA-GAN approach with several state-of-the-art methods on a number of remote sensing scenes, and the experimental results consistently demonstrate the effectiveness of the proposed MA-GAN. For study replication, the source code will be released at: https://github.com/ZhihaoWang1997/MA-GAN. [ABSTRACT FROM AUTHOR]

As the only natural satellite of the Earth, the Moon provides vital location resources and supportive environment for Earth observations, and the Moon-based Earth observation (MEO) has unparalleled advantages in global climate change and large-scale phenomena. The ocean plays an important role in regulating climate and global water and carbon cycle. With an attempt to explore the feasibility of MEO-based marine environment monitoring, this study aimed to investigate the observing geometry and revisiting frequency of the MEO-based ocean color remote sensing and further to explore its quantitative application potentials. Results showed that MEO-based ocean color remote sensing, capturing the Earth on an hourly basis, could observe most part of the ocean for over five times per day; however, both solar zenith angle and view zenith angle were high at high-latitude regions; atmospheric reflectance accounted for most of sensor-measured signal, especially at high solar and view zenith angle, while surface-reflected glint reflectance was also notable at low solar zenith angle; and the remote sensing reflectance retrieved from MEO-based ocean color remote sensing could be used for chlorophyll retrieval. In further studies, more efforts should be paid on how to accurately retrieve remote sensing reflectance at high solar and view zenith angle, which would improve the application capability of MEO for polar regions. Overall, this study demonstrated the great potentials of MEO-based ocean color remote sensing, and MEO would be a new observing perspective and long-term consistent data source for marine environment monitoring. [ABSTRACT FROM AUTHOR]

The Ocean and Land Color Instrument (OLCI) on Sentinel-3 is one of the most advanced ocean color satellite sensors for aquatic environment monitoring. However, limited studies have been focused on a comprehensive assessment of atmospheric correction (AC) methods for OLCI. In an attempt to fill the gap, this study evaluated seven different AC methods for OLCI using global automatic in situ observations from Aerosol Robotic Network-Ocean Color (AERONET-OC). Results showed that the POLYnomial-based algorithm applied to MERIS (POLYMER) had the best performance for bands with wavelength ≤ 443 nm, and the SeaDAS method based on 779 and 865 nm was the best for longer spectral bands; however, SeaDAS (SeaWiFS Data Analysis System) processing algorithm based on 779 and 1020 nm, as well as 865 and 1020 nm, obtained degraded AC performance; Case 2 Regional CoastColor (C2RCC) also produced large uncertainties; Baseline AC (BAC) method might be better than SeaDAS method; and simple subtraction method was the worst except for turbid waters. POLYMER and C2RCC underestimated high remote sensing reflectance (Rrs) at red and green bands; SeaDAS method based on 779 and 865 nm held an advantage for clear waters over the other two band combinations, while their difference turned small for turbid waters. AC uncertainties generally impacted the performance of chlorophyll retrievals. POLYMER outperformed other methods for chlorophyll retrieval. This study provides a good reference for selecting a suitable AC method for aquatic environment monitoring with Sentinel-3 OLCI. [ABSTRACT FROM AUTHOR]

Remotely sensed nighttime light (NTL) data provides an opportunity to observe human economic activities at night. However, existing NTL remote sensing data does not sufficiently reflect human activities at a fine resolution. In this study, we obtain a set of NTL data comprising volunteered passenger aircraft nighttime remote sensing (VPAN-RS) imagery by adopting a passenger plane as the remote sensing platform and using low-cost portable photography equipment as sensors. This method demonstrates the advantages of VPAN-RS data in high spatial resolution, frequent revisits, and low cost, which can supplement existing NTL data. The dataset acquired in this study covers 16 cities in China and one city in Japan, with a spatial resolution up to meter level. The data acquisition method and processing workflow are introduced in this article. Quality assessment shows that the reprojection error of the dataset falls within the range from 5–10 pixels, and the geometric error can be within 15 m. Via a comparison to reference data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor of the Suomi-National Polar-Orbiting Partnership (NPP) polar-orbiting satellite, we found that the VPAN-RS data provides more details of city lighting. Additionally, the radiance of the VPAN-RS data suitably fits that of monthly composite NPP/VIIRS data. We further outline an application example of VPAN-RS nighttime imagery, examine the unique challenges associated with VPAN-RS data with a focus on the influence of the image acquisition method on the data quality, and provide an outlook for the future of VPAN-RS data. We conclude that VPAN-RS NTL images are an effective data source for nighttime earth observations and have the potential for various applications. [ABSTRACT FROM AUTHOR]

Due to the detailed spectral information through hundreds of narrow spectral bands provided by hyperspectral image (HSI) data, it can be employed to accurately classify diverse materials of interest, which is one of the core applications of hyperspectral remote sensing technology. In recent years, with the rapid development of deep learning, convolutional neural networks (CNNs) have been successfully applied in many fields, including HSI classification. However, the random gradient descent-based parameter updating scheme is too general and leading to the inefficiency of CNN models. Moreover, the high dimensionality and limited training samples of HSI data also exacerbate the overfitting problem. To tackle these issues, in this article, a novel deep network with multilayer and multibranch architecture, named 3-D Gabor CNN (3DG-CNN), is proposed for HSI classification. More precisely, since the predefined 3-D Gabor filters in multiple scales and orientations could well characterize the internal spatial–spectral structure of HSI data from various perspectives, the 3-D Gabor-modulated kernels (3-D GMKs) are employed to replace the random initialization kernels. Moreover, the specially designed multibranch architecture enables the network to better integrating the scalable property of 3-D Gabor filters; thus, the representative ability and robustness of the extracted features can be greatly improved. Alternatively, the number of network parameters is substantially reduced due to the incorporation of 3-D Gabor modulation, relieving the training complexity and also alleviating the training process from overfitting. Experimental results on four real HSI datasets (including two newly released ones in the literature) have demonstrated that the proposed 3DG-CNN model can achieve better performance than several widely used machine-learning-based and deep-learning-based approaches. For the sake of reproducibility, the codes of the proposed 3DG-CNN model are available at http://jiasen.tech/papers/. [ABSTRACT FROM AUTHOR]

Hyperspectral images encompass abundant information and provide unique characteristics for material classification. However, the labeling of training samples can be challenging in hyperspectral image classification. To address this problem, this study proposes a framework named flexible Gabor-based superpixel-level unsupervised linear discriminant analysis (FG-Su ULDA) to extract the most informative and discriminating features for classification. First, a number of 3-D flexible Gabor filters are rigorously designed using an asymmetric sinusoidal wave to sufficiently characterize the spatial–spectral structure in hyperspectral images. Then, an unsupervised linear discriminant analysis strategy guided by the entropy rate superpixel (ERS) segmentation algorithm, called Su ULDA, is skillfully introduced to reduce the extracted large amount of FG features. The Su ULDA method not only boosts the classification capability but also increases the peculiarity of features, with the aid of superpixel information. Finally, the achieved features are imported to the popular support vector machine classifier. The proposed FG-Su ULDA framework is applied to four real hyperspectral image data sets, and the experiments constantly prove that our FG-Su ULDA is superior to several state-of-the-art methods in both classification performance and computational efficiency, especially with scarce training samples. The codes of this work are available at http://jiasen.tech/papers/ for the sake of reproducibility. [ABSTRACT FROM AUTHOR]

Due to the importance in many military and civilian applications, hyperspectral anomaly detection has attracted remarkable interest. Low-rank representation (LRR)-based anomaly detectors use the low-rank property to represent background pixels, and pixels that cannot be well represented are detected as anomalies. The ability of an LRR-based detector to separate background pixels and anomalous pixels depends on the dictionary representation ability, which usually can be enhanced by designing a proper prior for dictionary representation coefficients and constructing a better dictionary. However, it is not easy to handcraft effective and meaningful regularizers for dictionary coefficients. In this article, we propose a novel anomaly detection algorithm that uses a plug-and-play prior for representation coefficients and constructs a new dictionary based on clustering. Instead of cumbersomely handcrafting a regularizer for representation coefficients, we propose solving the anomaly detection problem using the plug-and-play framework, which enables us to plug state-of-the-art priors for representation coefficients. An effective convolutional neural network (CNN) denoiser is plugged into our framework to fully exploit the spatial correlation of representation coefficients. We also propose a modified background dictionary construction method, which carefully includes background pixels and excludes anomalous pixels from clustering results. We refer to the proposed anomaly detection method as plug-and-play denoising CNN regularized anomaly detection (DeCNN-AD) method. Extensive experiments were performed on five data sets in a comparison with eight state-of-the-art anomaly detection methods. The experimental results suggest that the proposed method is effective in anomaly detection and can produce better anomaly detection results than that of the comparison methods. The codes of this work will be available at https://github.com/FxyPd for the sake of reproducibility. [ABSTRACT FROM AUTHOR]

In the hyperspectral image, each pixel corresponds to a small area on the Earth’s surface and represents the intrinsic characteristic of objects, which can be applied for recognition of land covers. Nevertheless, hyperspectral image processing should face some critical issues, and a small sample set problem may be the most challenging one in the research. Deep learning (DL), which has successfully been applied in many fields, has also been introduced for hyperspectral image classification. However, the large gap between the massive parameters to be tuned and limited labeled samples can lead to overfitting scenario, inevitably deteriorating the generalization ability of the DL model. In this article, a lightweight convolutional neural network (LWCNN) is proposed for hyperspectral image classification to mainly tackle the small sample set problem. Especially, spatial–spectral Schroedinger eigenmaps (SSSE) feature extraction is first adopted to obtain the joint spatial–spectral information, and the compressed dimensionality could significantly reduce the number of parameters in the following DL model. Second, a dual-scale convolution (DSC) module is carefully designed to address the SSSE features from a 1-D vector viewpoint (the number of parameters is further decreased), and the DSC procedure is successively employed to obtain the hierarchical structure description that could represent data distribution from different aspects. Subsequently, the feature vectors from all DSC layers are separately filtered by a new bichannel fusion (BCF) module, which could well encode both the intrinsic and contextual information inside DSC features. Finally, the filtered features are concatenated together and imported into a global average pooling classifier to achieve the predicted probability of each category. Experimental results on three famous hyperspectral image data sets illustrate that the developed LWCNN approach is advantageous in both the efficiency and robustness sides for hyperspectral image classification tasks and outperforms other state-of-the-art methods (both traditional-based and DL-based) with very limited labeled samples. [ABSTRACT FROM AUTHOR]

Due to the 3-D nature of hyperspectral images, as well as the spatial properties (such as regularity and continuity) of land covers, many 3-D feature extraction operators have been designed to fully exploit the joint spatial–spectral information. However, the large amount of obtained features can suffer from the “curse of dimensionality” problem, especially for the small training sample set. Moreover, various spatial–spectral features can represent the characteristics of the hyperspectral image from different aspects. In this paper, a multiple 3-D feature fusion framework (M3DF3) has been proposed for hyperspectral image classification. First, we extend the 2-D Gabor surface feature into 3-D (3DSF) domains to comply with the spatial–spectral structure of the hyperspectral image, which is directly applied on the original hyperspectral image instead of the Gabor features. Second, three 3-D feature extraction methods, including the 3-D morphological profile, the 3-D local binary pattern, and the proposed 3DSF, that, respectively, characterize the hyperspectral image from three different angles, i.e., morphology, local dependence, and shape smoothness, are fused under a multitask sparse representation framework to take full advantage of the multiple 3-D features together. The proposed M3DF3 approach was fully tested on three real-world hyperspectral image data, i.e., the widely used Indian Pines, Pavia University, and Houston University. The results show that our method can achieve as high as 68.22%, 79.44%, and 72.84% accuracies, respectively, even when only few samples, i.e., three samples per class, are used for training. [ABSTRACT FROM AUTHOR]

Due to the high spectral dimensionality of hyperspectral images as well as the difficult and time-consuming process of collecting sufficient labeled samples in practice, the small sample size scenario is one crucial problem and a challenging issue for hyperspectral image classification. Fortunately, the structure information of materials, reflecting region of homogeneity in the spatial domain, offers an invaluable complement to the spectral information. Assuming some spatial regularity and locality of surface materials, it is reasonable to segment the image into different homogeneous parts in advance, called superpixel, which can be used to improve the classification performance. In this paper, a superpixel-based multitask learning framework has been proposed for hyperspectral image classification. Specifically, a set of 2-D Gabor filters are first applied to hyperspectral images to extract discriminative features. Meanwhile, a superpixel map is generated from the hyperspectral images. Second, a superpixel-based spatial–spectral Schroedinger eigenmaps (S4E) method is adopted to effectively reduce the dimensions of each extracted Gabor cube. Finally, the classification is carried out by a support vector machine (SVM)-based multitask learning framework. The proposed approach is thus termed Gabor S4E and SVM-based multitask learning (GS4E-MTLSVM). A series of experiments is conducted on three real hyperspectral image data sets to demonstrate the effectiveness of the proposed GS4E-MTLSVM approach. The experimental results show that the performance of the proposed GS4E-MTLSVM is better than those of several state-of-the-art methods, while the computational complexity has been greatly reduced, compared with the pixel-based spatial–spectral Schroedinger eigenmaps method. [ABSTRACT FROM PUBLISHER]

The local binary pattern (LBP) is a simple and efficient texture descriptor for image processing. Recently, LBP has been introduced for feature extraction of hyperspectral imagery. Specifically, the LBP codes are extracted from the 2-D band images to capture the spatial correlation among neighboring pixels, and then the statistical histogram features from all bands, which could estimate the underlying distribution in local area, are concatenated together for pixel-wise classification. However, since hyperspectral imagery contains rich spectral and spatial information, which is actually a 3-D data cube, the 2-D LBP (2-DLBP) model cannot fully exploit the joint spectral–spatial structure. In this paper, the 2-DLBP has been extended into 3-D LBP (3-DLBP) model through forming a 3-D regular octahedral frame to characterize the spectral–spatial relationship. In order to reflect the local continuous property of hyperspectral data in both the spectral and spatial domains, while ensuring the rotational invariance of the 3-DLBP model, the code patterns of 3-DLBP model have been divided into eight groups (including seven groups of “dense” patterns and one group of “nondense” patterns) based on the consistency of spectral–spatial topology structure. Specifically, the patterns in seven “dense” groups correspond to the microstructures in the 3-D domains (such as spots, edges, and flat areas), which has a high percentage in all the 3-DLBP patterns, while the rest patterns are aggregated and treated as the “nondense” patterns. The proposed method is thus called 3-D dense LBP (3-D2LBP) model. Moreover, instead of taking zero as the hard threshold, a slack variable has been introduced to enable the difference between the central pixel and the neighboring ones varying in a small interval, which could greatly decrease the impact of spectral variability and noise, and the discriminative power of the features has been further boosted. The slack threshold-based 3-D2LBP model is named ST-3-D2LBP. A series of experiments is conducted on three real hyperspectral imageries to demonstrate the effectiveness of the proposed two 3-D2LBP-based methods. The experimental results show that the performance of the proposed ST-3-D2LBP is significantly superior to that of 2-DLBP, which is also better than the 3-D2LBP model and several state-of-the-art hyperspectral classification methods. [ABSTRACT FROM PUBLISHER]

Fitting polarimetric synthetic aperture radar (PolSAR) data with adaptive scattering models is a promising way to mitigate the deficiencies of the model-based decomposition. Recently, Lee et al. have proposed a generalized decomposition model with several adaptive parameters, whereas the generalized model introduces too much freedom to be solved. In this paper, based on the Lee generalized decomposition model, an adaptive two-component decomposition model is proposed. The PolSAR coherency matrix is represented as the sum of two scattering mechanisms: coherent ground scattering and incoherent volume scattering. The proposed model is under three assumptions: 1) Surface and double scattering are coherent; 2) surface and double scattering are integrated as the ground scattering; and 3) the average polarimetric orientation angle (POA) of the volume (or Bragg) scattering is zero. As the proposed model is very difficult to solve directly, we adopted the exhaustion technique to find the best fit parameter set. The proposed model has three advantages: 1) It can successfully avoid the negative power problem; 2) it is considered without the assumption of reflection symmetry; and 3) the dominant scattering mechanism criterion is not needed in the process of model inversion. However, the proposed model has two disadvantages: 1) the attribution of the volume model becomes ambiguous; and 2) the assumption that sets the POA of the Bragg scattering component to zero is inconsistent with the actual scattering mechanism when there is a slope in the rough surface. The polarimetric AIRSAR L-band data of San Francisco and ESAR L-band data of Oberpfaffenhofen were used to show the efficiency of the proposed decomposition model. Statistical properties of typical areas showed that, except the sea surface and the urban area with building orientation angle about 45°, the proposed model fits the PolSAR data very well. [ABSTRACT FROM PUBLISHER]

The assumption of reflection symmetry is one of the major deficiencies for model-based decompositions. The introduction of helix scattering components can mitigate some of the impact of this assumption while simultaneously generating some new negative power problems. The helix mechanism has been developed in the Krogager coherent target decomposition where there is no negative power problem. Why do these problems happen in model-based decompositions? In this paper, we review the Krogager decomposition based on the coherency matrix form of the unified CTD model and come to the conclusion that the imaginary part of T23 is an overestimate of the helix contribution, which is the main cause of the problem. Furthermore, we review the techniques of symmetric scatterer transformation in the Cameron and Huynen decompositions and expand them to the multilook coherency matrix. Two new models of the nonlinear programming problem are proposed to get the transformed coherency matrix: 1) by subtracting the asymmetric component; and 2) by the procedure of con-diagonalization. A new parameter is established to measure the degree of reflection asymmetry of the coherency matrices. Experimental results in the L-band Airborne SAR (AIRSAR) San Francisco data show that the two new models can reduce the correlations between copolarized and cross-polarized channels significantly. In this case, the assumption of reflection symmetry is reasonable for the transformed coherency matrices, and negative power problems related to helix scattering no longer exist in the model-based decompositions; hence, the helix scattering component is not needed. Lastly, the Freeman and Durden decomposition has been applied to the transformed coherency matrices. Experimental results showed that the decomposition results can reflect the predominant characteristics of the ground objects. [ABSTRACT FROM PUBLISHER]

Due to the diversity of geographical objects, it makes great sense to introduce multiscale segmentation/representation into the analysis and interpretation of high-spatial-resolution remote sensing images. However, with the increasing use of high-resolution images, traditional multiscale segmentation methods gradually show their lack in efficiency, particularly when handling large-scale images. In this paper, a novel bilevel scale-sets model (BSM) is proposed for multiscale region-based representation of large-scale remote sensing images. In the BSM, first, an image is divided into blocks with overlapped margins, and a low-level scale-sets model is blockwisely implemented. Second, a segmentation result is obtained by retrieving and mosaicking the blockwise segmentation results, based on which a high-level scale-sets model is implemented covering the whole image. To further improve the efficiency of the BSM, a parallel implementation is presented for the blockwise scale-sets model. In the experiments, first, the effectiveness of the BSM is validated using a WorldView2 image covering a coastal area of Shenzhen, where the BSM obtains accurate multiscale representation results without any mosaic artifacts. Then, the efficiency of the BSM is demonstrated by comparing with the state-of-the-art multiscale segmentation method, i.e., the one integrated in the commercial software eCognition v9.2, where the proposed BSM takes about 7 min to process a 24 000 $\times$ 24 000 multispectral ZY3 image and is two to three times faster than the competing method. [ABSTRACT FROM PUBLISHER]

Building orientation with respect to the radar look direction has a critical influence on the interpretation of multilook polarimetric synthetic aperture radar (PolSAR) data in urban areas. In this paper, its impacts on polarimetric orientation angle (POA) estimation and model-based decomposition are discussed. The discussion begins with the analysis of the general double-bounce scattering model, of which the characteristics are dependent on the electromagnetic and geometric parameters of the related dihedral structure. Then, for multilook PolSAR data, the polarimetric scattering mechanism in urban areas is modeled by two double-bounce scatterings from two orthogonal dihedral structures. From the model, the impacts of the building orientation on POA estimation can be revealed. With the increase of the building orientation, the POA difference between the two dihedral structures increases gradually, and the feasibility to estimate the building orientation via the estimated POA is reduced dramatically. Upon further analysis, we illustrate the impacts on the model-based decomposition. With the increase of the building orientation, the dominant scattering mechanism labeling technique based on the model-based decompositions will gradually become invalid. Moreover, the processing of POA compensation, which is helpful in reducing the impacts of the building orientation, also becomes invalid when the building orientation increases to a certain value. At last, three L-band data sets of San Francisco acquired by AIRSAR are used to verify the inferences. The experimental results show that, for L-band PolSAR data in urban areas, when the radar look angle is around 45, the threshold of building orientation for the validity of dominant scattering mechanism labeling is about ±3, and for the POA compensation, the threshold is about ±12. [ABSTRACT FROM AUTHOR]

The large amount of spectral and spatial information contained in hyperspectral imagery has provided great opportunity to effectively characterize and identify the surface materials of interest. As a novel feature extraction technique, a series of Gabor wavelet filters with different scales and frequencies was applied on hyperspectral data to extract spectral–spatial-combined features, which produced impressive performance on pixel-oriented classification. However, the incredibly large number of Gabor features could cause too much burden for onboard computation, limiting the efficiency of the method. To make matters worse, due to the nonhomogeneous spatial distribution of materials as well as the different characteristics of the constructed Gabor filters, some Gabor features could have a smaller or even negative impact on material representation, deteriorating the classification accuracy eventually. In this paper, a Gabor cube selection based multitask joint sparse representation approach, abbreviated as GS-MTJSRC, was proposed for hyperspectral image classification. First, based on the Fisher discrimination criterion, the most representative Gabor cubes for each class were picked out. Next, under multitask joint sparse representation framework, a coefficient vector could be obtained for each test sample with the selected Gabor cube features, which could be directly used for the following residual-based classification. Experimental results on three real hyperspectral data sets with different characteristics and spatial resolutions demonstrated the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Coherence loss is a critical issue in interferometric synthetic aperture radar geodesy, particularly when short-wavelength radar images are used to monitor earthquake deformation, and it may result in isolated fringes in an interferogram. The conventional unwrapping algorithms may incompletely unwrap or wrongly estimate the integer jumps between isolated fringes. In this paper, we propose a novel method to connect the isolated fringes in earthquake slip inversion. We use multiple starting points to unwrap the interferogram and then solve the integer ambiguities among the starting points by a dislocation-model-based integer least squares estimator. This estimator allows us to provide a quantitative evaluation of the reliability of the integer solutions in terms of two indicators (the success rate and residual ratio). The algorithm is robust to a certain degree of data noise and fault geometry error, as tested. Simulated experiments and case studies demonstrate that the proposed method can give better unwrapping results than the conventional approaches such as the minimum-cost flow (MCF), statistical-cost network-flow algorithm for phase unwrapping (SNAPHU), and iterative forms of MCF and SNAPHU with the assistance of a slip model. The earthquake slip inversion therefore benefits from the more accurate unwrapping results. [ABSTRACT FROM AUTHOR]

Through imaging the same spatial area by hyperspectral sensors at different spectral wavelengths simultaneously, the acquired hyperspectral imagery often contains hundreds of band images, which provide the possibility to accurately analyze and identify a ground object. However, due to the difficulty of obtaining sufficient labeled training samples in practice, the high number of spectral bands unavoidably leads to the problem of a “dimensionality disaster” (also called the Hughes phenomenon), and dimensionality reduction should be applied. Concerning band (or feature) selection, conventional methods choose the representative bands by ranking the bands with defined metrics (such as non-Gaussianity) or by formulating the band selection problem as a clustering procedure. Because of the different but complementary advantages of the two kinds of methods, it can be beneficial to use both methods together to accomplish the band selection task. Recently, a fast density-peak-based clustering (FDPC) algorithm has been proposed. Based on the computation of the local density and the intracluster distance of each point, the product of the two factors is sorted in decreasing order, and cluster centers are recognized as points with anomalously large values; hence, the FDPC algorithm can be considered a ranking-based clustering method. In this paper, the FDPC algorithm has been enhanced to make it suitable for hyperspectral band selection. First, the ranking score of each band is computed by weighting the normalized local density and the intracluster distance rather than equally taking them into account. Second, an exponential-based learning rule is employed to adjust the cutoff threshold for a different number of selected bands, where it is fixed in the FDPC. The proposed approach is thus named the enhanced FDPC (E-FDPC). Furthermore, an effective strategy, which is called the isolated-point-stopping criterion, is developed to automatically determine the appropriate number of bands to be selected. That is, the clustering process will be stopped by the emergence of an isolated point (the only point in one cluster). Experimental results on three real hyperspectral data demonstrate that the bands selected by our E-FDPC approach could achieve higher classification accuracy than the FDPC and other state-of-the-art band selection techniques, whereas the isolated-point-stopping criterion is a reasonable way to determine the preferable number of bands to be selected. [ABSTRACT FROM AUTHOR]