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Spectral unmixing is one of the prime topics in hyperspectral image analysis, as images often contain multiple sources of spectra. Spectral variability is one of the key factors affecting unmixing accuracy, since spectral signatures are affected by variations in environmental conditions. These and other factors interfere with the accurate discrimination of source type. Several spectral mixing models have been proposed for hyperspectral unmixing to address the spectral variability problem. The interpretation for the spectral variability of these models is usually insufficient, and the unmixing algorithms corresponding to these models are usually classic unmixing techniques. Hyperspectral unmixing algorithms based on deep learning have outperformed classic algorithms. In this paper, based on the typical extended linear mixing model and the perturbed linear mixing model, the scaled and perturbed linear mixing model is constructed, and a spectral unmixing network based on this model is constructed using fully connected neural networks and variational autoencoders to update the abundances, scales, and perturbations involved in the variable endmembers. Adding spatial smoothness constraints to the scale and adding regularization constraints to the perturbation improve the robustness of the model, and adding sparseness constraints to the abundance determination prevents overfitting. The proposed approach is evaluated on both synthetic and real data sets. Experimental results show the superior performance of the proposed method against other competitors. [ABSTRACT FROM AUTHOR]

Nowadays, some tensor-based hyperspectral anomaly detection (HAD) approaches are still insufficient in utilizing the spatial–spectral structure information of hyperspectral images (HSIs), resulting in the inability to isolate the background and abnormal targets well. In this letter, an enhanced tensor low-rank representation (ETLR) learning model is proposed for HAD. Specifically, the original 3-D HSI data is first decomposed into a structural background component, an anomaly component, and a noise component. Among them, with the help of multisubspace learning technology, the structural background component is reformulated by the t-product of the background dictionary tensor and the corresponding coefficient tensor. Then, the tensor nuclear norm (TNN) is adopted to preserve the global low-rank property of the background component in both spatial and spectral dimensions. For the abnormal component, an $\ell _{2,1,1}$ -norm is designed to enhance the group sparsity of abnormal pixels. For the noise component, a tensor $F$ -norm constraint is imposed to suppress the confusion of noise and anomalies. Meanwhile, a robust dictionary tensor that can adequately characterize the background is constructed by using tensor robust principal component analysis (TRPCA). Furthermore, to reduce the interference of redundant information on detection accuracy, the optimal clustering framework (OCF) method is utilized for band selection. Finally, extensive experiments on one simulated and three real HSI datasets confirm that our algorithm is superior to current HAD algorithms. [ABSTRACT FROM AUTHOR]

The reliability of feature matching can decide the accuracy and robustness of the feature-based registration result. Aiming at the problem that the number of final feature matches preserved by many popular outlier removal methods is small, and the position accuracy of final feature matches is not high enough, we propose an accurate and robust image registration framework based on outlier removal and feature point adjustment in this paper. This framework increases the number and improves the position accuracy of inliers while eliminating most outliers. The increased number of inliers improves the robustness of image registration, and high accurate inliers improves the accuracy of image registration. Firstly, the initial feature matches are extracted by a commonly used feature-based registration method, such as the scale-invariant feature transform (SIFT)-based method. Then, outliers of the initial feature matches are eliminated by a frequency domain similarity measure, called PHase-based Structural SIMilarity (PH-SSIM) proposed in this paper. Considering the inherent error of the feature matches that still exist after the outlier elimination, a PH-SSIM-based feature point adjustment strategy is designed to fine-adjust the position of the preserved feature points in the reference image. Finally, the registration parameters are calculated by the fine-adjusted feature matches. The proposed framework has been evaluated by several remote sensing images with different resolution, grey-scale, texture, and scene, and compared with four state-of-the-art image registration methods. Experimental result demonstrates the high accuracy and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Autoencoder is an efficient technique for unsupervised feature learning, which can be applied to hyperspectral unmixing. In this letter, we present an autoencoder network with adaptive abundance smoothing (AAS) to solve the challenges of previous techniques. Specifically, the proposed method uses a multilayer encoder to obtain the abundance and a single-layer decoder to reconstruct the image. The AAS algorithm tackles the outliers by exploiting the spatial–contextual information and can be adaptive for each pixel. Moreover, the softmax function is used as the encoder output function with the help of L1/2 regularization to produce sparse output. Experimental results of the synthetic and real data reveal the superior performance of the proposed method against other competitors. [ABSTRACT FROM AUTHOR]

In recent years, low-rank representation (LRR) has attracted considerable attention in the field of hyperspectral anomaly detection. The main objective of LRR-based methods is to extract anomalies from the complex background. However, the presence of anomalies in the background dictionary can lower the detection performance. In this letter, a novel method is proposed for hyperspectral anomaly detection based on the LRR model. This method facilitates the discrimination between the anomalous targets and background by utilizing a novel dictionary and an adaptive filter based on the local outlier factor (LOF). In order to exclude the potential anomalies from the dictionary, the ranking of LOF scores for each pixel is adapted to select the potential background pixels as dictionary atoms. A filter that explores the intrinsic spatial structure is designed to enhance the differences between the anomalies and the background pixels. The experimental results that conducted on three real-world data sets demonstrate that the proposed method achieves a better performance than several state-of-the-art hyperspectral anomaly detection methods. [ABSTRACT FROM AUTHOR]

Hyperspectral band selection (BS) aims to select a subset of bands from the original image cube for subsequent tasks, such as pixel classification. In this paper, we propose a novel unsupervised BS method, termed the representativeness and redundancy-based BS (RRBS) method, by measuring the representativeness and redundancy of the selected bands. The intuitive motivation is to find a subset of bands, which represents the dataset and has low redundancy. The desired bands are obtained sequentially. In each round of lookup, two novel selection criteria based on orthogonal subspace projection are designed for searching the bands that not only well represent the unselected bands but also lowly correlate with the selected bands. Additionally, kernel tricks are used to develop a nonlinear version of the linear selection criteria. Both the linear and nonlinear selection criteria can explicitly evaluate the representativeness and redundancy simultaneously, and they are also robust to noisy bands. The experimental results verify that the proposed method yields excellent classification performance even selecting very a limited number of bands. [ABSTRACT FROM AUTHOR]

Due to significant geometric distortions and illumination differences, developing techniques for high precision and robust multisource remote sensing image registration poses a great challenge. This article presents an iterative image registration approach, called iterative scale-invariant feature transform (ISIFT) for remote sensing images, which extends the traditional scale-invariant feature transform (SIFT)-based registration system to a close-feedback SIFT system that includes a rectification feedback loop to update rectified parameters in an iterative manner. Its key idea uses consistent feature point sets obtained by maximum similarity to calculate new alignment parameters to rectify the current sensed image and the resulting rectified sensed image is then fed back to update and replace the current sensed image as a new sensed image to reimplement SIFT for next iteration. The same process is repeated iteratively until an automatic stopping rule is satisfied. To evaluate the performance of ISIFT, both the simulated and real images are used for experiments for the validation of ISIFT. In addition, several data sets are particularly designed to conduct a comparative study and analysis with existing state-of-the-art methods. Furthermore, experiments with different rotation are also performed to verify the adaptability of ISIFT under different rotation distortions. The experimental results demonstrate that ISIFT improves performance and produces better registration accuracy than traditional SIFT-based methods and existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) is a crucial technique for exploiting remotely sensed hyperspectral data, which aims at estimating a set of spectral signatures, called endmembers and their corresponding proportions, called abundances. The performance of HU is often seriously degraded by various kinds of noise existing in hyperspectral images (HSIs). Most of existing robust HU methods are based on the assumption that noise or outlier only exists in one kind of formulation, e.g., band noise or pixel noise. However, in real-world applications, HSIs are unavoidably corrupted by noisy bands and noisy pixels simultaneously, which require robust HU in both the spatial dimension and spectral dimension. Meanwhile, the sparsity of abundances is an inherent property of HSIs and different regions in an HSI may possess various sparsity levels across locations. This article proposes a correntropy-based spatial-spectral robust sparsity-regularized unmixing model to achieve 2-D robustness and adaptive weighted sparsity constraint for abundances simultaneously. The updated rules of the proposed model are efficient to be implemented and carried out by a half-quadratic technique. The experimental results obtained by both synthetic and real hyperspectral data demonstrate the superiority of the proposed method compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This letter presents a superpixel-based dual window RX (SPDWRX) anomaly detection (AD) algorithm that uses superpixel segmentation (SPS) to adaptively determine the dual window for local RX (LRX) detection, rather than using a fixed dual window. The main premise of SPDWRX is to first divide the hyperspectral image into multiple superpixels and then extend the minimum bounding rectangle to determine the background of each superpixel. Finally, LRX AD is conducted on each pixel in the same superpixel using the same background. Furthermore, a fine SPS method is proposed based on the entropy rate superpixel to quickly obtain uniform superpixels. The experimental results show that the proposed SPDWRX method can significantly improve the detection speed and slightly improve the detection performance, and the modified SPS can further improve the detection performance of SPDWRX. [ABSTRACT FROM AUTHOR]

The orthogonal-projection-based band selection (OPBS) algorithm is one of the newly proposed band selection methods. In this letter, we present a nonlinear version of the OPBS method, which is denoted as the Kernel-OPBS method. The OPBS method selects the desired bands one by one, and in each round of lookup, it chooses the band that has the maximum distance to the hyperplane spanned by the currently selected bands. Extending this algorithm to a feature space associated with the original input space through a certain nonlinear mapping function can provide a nonlinear version of the OPBS algorithm. Although it is basically intractable to compute the mapped bands due to the high dimensionality of the feature space produced by the nonlinear mapping function, the selection criterion of the Kernel-OPBS method is actually related to only the inner products of the mapped bands; thus, the kernel function can be applied and it is unnecessary to define the nonlinear mapping function. Experimental results on different data sets demonstrate that the selected bands obtained by the Kernel-OPBS method can achieve higher pixel classification performances than that by the OPBS method. [ABSTRACT FROM AUTHOR]

Infrared small target detection is one of the most important parts of infrared search and tracking (IRST) system. Generally, the small and dim target is of low signal-to-noise ratio and buried in the complicated background and heavy noise, which makes it extremely difficult to be detected with low false alarm rates. To solve this problem, we propose a small target detection method based on multiscale local contrast measure. Different from conventional methods, we novelly measure the local contrast from two aspects: local dissimilarity and local brightness difference. First, we present a new dissimilarity measure called the local energy factor (LEF) to describe the dissimilarity between the small targets and their surrounding backgrounds. Second, the feature of the brightness difference between the small targets and the backgrounds is utilized. Afterward, the local contrast is measured by taking both features of the above into account. Finally, an adaptive segmentation method is applied to extract the small targets from the backgrounds. Extensive experiments on real test data set demonstrate that our approach outperforms the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) is a crucial technique for exploiting remotely sensed hyperspectral data, which aims to estimate a set of spectral signatures, called endmembers and their corresponding proportions, called abundances. Nonnegative matrix factorization (NMF) and its various robust extensions have been widely applied to HU. Most existing robust NMF methods consider that noises only exist in one kind of formulation. However, the hyperspectral images (HSIs) are unavoidably corrupted by noisy bands and noisy pixels simultaneously in the real applications. This paper proposes a novel spectral–spatial robust NMF model by incorporating $\ell _{2,1}$ norm and $\ell _{1,2}$ norm, which achieves robustness to band noise and pixel noise simultaneously. The Huber’s M-estimator is integrated into the proposed model to achieve better assignations of weights for each pixel and band with various noise intensities, which avoids the singularity problem and effectively improves the unmixing performance. The elegant updating rules of the proposed spectral–spatial robust model are also efficiently learned and provided. Experiments are conducted on both synthetic and real hyperspectral data sets. The experimental results demonstrate the effectiveness of the proposed methods in unmixing performance. [ABSTRACT FROM AUTHOR]

Kernel nonnegative matrix factorization (KNMF) is an extension of NMF designed to capture nonlinear dependence features in data matrix through kernel functions. In KNMF, the size of the kernel matrices is closely associated with the input data matrix, of which the calculation consumes a large amount of memory and computing resource. When applied on large-scale hyperspectral data, KNMF often meets the bottleneck of memory and may cause the overflow of memory. And when dealing with dynamically acquired data, KNMF requires recomputation of the whole data set when newly acquired data arrived, which produces huge memory and computing resource requirements. To reduce the usage of memory and improve the computational efficiency when applying KNMF on large scale and dynamic hyperspectral data, we extend KNMF by introducing partition matrix theory and considering the relationships among dividing blocks. The decomposition results of hyperspectral data are derived from much smaller scale matrices containing the formerly achieved results and the newly data blocks incrementally. In this paper, we propose an incremental KNMF (IKNMF) to reduce the computing requirements for large-scale data in hyperspectral unmixing. An improved IKNMF (IIKNMF) is also proposed to further improve the abundance results of IKNMF. Experiments are conducted on both synthetic and real hyperspectral data sets. The experimental results demonstrate that the proposed methods can effectively save memory resources without degrading the unmixing performance and the proposed IIKNMF can achieve better abundance results than IKNMF and KNMF. [ABSTRACT FROM AUTHOR]

Band selection (BS) tries to find a few useful bands to represent the whole hyperspectral image cube. This letter proposes a novel unsupervised BS method based on the band correlation analysis (BCA). The BCA method tries to find a subset of bands that can well represent the whole image data set. To avoid the exhaustive search, the BCA method iteratively adds the band with the good representative ability and low redundancy into the selected band set, until the sufficient quantity of bands has been obtained. The redundancy and the representative ability of one band are computed by its correlation with the currently selected bands and the remaining unselected bands, respectively. Through constructing a correlation matrix of total bands, the BCA method can find the bands that with large amounts of information and low redundancy, which ensures that the selected bands are useful for the further applications like pixels classification. Experimental results on three different data sets demonstrate that the proposed method is very effective and can achieve the best performance among the competitors. [ABSTRACT FROM AUTHOR]

Band selection (BS) is a special case of the feature selection problem, and it tries to remove redundant bands and select a few informative and distinctive bands to represent the whole image cube. The maximum ellipsoid volume (MEV) method regards the band subset with the maximum volume as the optimal band combination. However, the MEV method cannot be directly applied for hyperspectral imagery due to the high dimensionality of the data sets. Therefore, we first combine MEV with the sequential forward search (SFS) and propose a new unsupervised BS method called MEV-SFS. Furthermore, a subtle relationship between the ellipsoid volume of the band set and the orthogonal projections (OPs) of the candidate bands is observed. Based on this relationship, we propose another equivalent method, namely, the OP-based BS (OPBS) method. OPBS is the fast version of MEV-SFS, and it has a better computational efficiency and the potential to determine the number of bands to be selected. We specifically explain the rationality of the MEV-based methods (MEV-SFS and OPBS) and illustrate their theoretical significance and physical meaning from different aspects. Theoretical analysis also demonstrates that OPBS can be regarded as a model or framework for BS, and thus, we further propose a third novel BS method named the OPBS-information divergence (OPBS-ID) method, which is a variant of OPBS. OPBS-ID can achieve a better classification performance than OPBS in many cases. Experimental results on different hyperspectral data sets demonstrate that the proposed methods have high computational efficiency, and the selected bands can achieve satisfactory classification performances. [ABSTRACT FROM AUTHOR]

Owing to significant geometric distortions and illumination differences, high precision and robust matching of multisource remote sensing images is a difficult task. To solve this, mutual information (MI)-based methods have been a preferred choice, as MI represents a measure of statistical dependence between the two images. However, MI only considers original grey information and neglects spatial information in the calculation of the probability distribution. In this paper, a novel similarity metric based on rotationally invariant regional mutual information (RIRMI) is proposed. The RIRMI metric is constructed by combining MI with a regional information based on the statistical relationship between rotationally invariant centre-symmetric local binary patterns of the images. The similarity metric based on RIRMI considers not only the spatial information, but the effect of the local grey variations and rotation changes on computing probability density function as well. The proposed method is tested on various simulated remote sensing images (5-30 m GSD) and real remote sensing images (2-30 m GSD) which are taken at different sensors, spectral bands, and times. Results verify that RIRMI is more robust and accurate than the common MI-based registration method. [ABSTRACT FROM AUTHOR]

Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. This paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. A causal sample covariance/correlation matrix is derived for local window background. As for the real-time sliding windows, theWoodbury identity is used in recursive update equations, which could avoid the calculation of historical information and thus speed up the processing. Furthermore, a background suppression algorithm is also proposed in this paper, which removes the current under test pixel from the recursively update processing. Experiments are implemented on a real hyperspectral image. The experiment results demonstrate that the proposed anomaly detector outperforms the traditional real-time local background detector and has a significant speed-up effect on calculation time compared with the traditional detectors. [ABSTRACT FROM AUTHOR]

The minimization problem of reconstruction error over large hyperspectral image data is one of the most important problems in unsupervised hyperspectral unmixing. A variety of algorithms based on nonnegative matrix factorization (NMF) have been proposed in the literature to solve this minimization problem. One popular optimization method for NMF is the projected gradient descent (PGD). However, as the algorithm must compute the full gradient on the entire dataset at every iteration, the PGD suffers from high computational cost in the large-scale real hyperspectral image. In this paper, we try to alleviate this problem by introducing a mini-batch gradient descent-based algorithm, which has been widely used in large-scale machine learning. In our method, the endmember can be updated pixel set by pixel set while abundance can be updated band set by band set. Thus, the computational cost is lowered to a certain extent. The performance of the proposed algorithm is quantified in the experiment on synthetic and real data. [ABSTRACT FROM AUTHOR]

Subpixel mapping technology can determine the specific location of different objects in the mixed pixel and effectively solve the uncertainty of the ground features spatial distribution in traditional classification technology. Existing methods based on linear optimization encounter the premature and local convergence of the optimization algorithm. This paper proposes a subpixel mapping method based on modified binary quantum particle swarm optimization (MBQPSO) to solve the above issues. The initial subpixel mapping imagery is obtained according to spectral unmixing results. We focus mainly on the discretization of QPSO, which is implemented by modifying the discrete update process of particle location, to minimize the objective function, which is formulated based on different connected regional perimeter calculating methods. To reduce time complexity, a target optimization strategy of global iteration combined with local iteration is performed. The MBQPSO is tested on standard test functions and results show that MBQPSO has the best performance on global optimization and convergent rate. Then, we analyze the proposed algorithm qualitatively and quantitatively by simulated and real experiment; results show that the method combined with MBQPSO and objective function, which is formulated based on the gap length between region and background, has the best performance in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Nonlinear unmixing, which has attracted considerable interest from researchers and developers, has been successfully applied in many real-world hyperspectral imaging scenarios. Hopfield neural network (HNN) machine learning has already proven successful in solving the linear mixture model; this study utilized an HNN machine learning approach to solve the generalized bilinear model (GBM) optimization problem. Two HNNs were constructed in a successive manner to solve respective seminonnegative matrix factorization problems intended for abundance and nonlinear coefficient estimation. In the proposed HNN-based GBM unmixing method, both HNNs evolve to stable states after a number of iterations to obtain unmixing results related to the states of neurons. In experiments on synthetic data, the proposed method showed more efficient performance in regard to abundance estimation accuracy than other GBM optimization algorithms, especially when given reliable endmember spectra. The proposed method was also applied to real hyperspectral data and still demonstrated notable advantages despite the obvious increase in unmixing difficulty. [ABSTRACT FROM PUBLISHER]

Like any other techniques for hyperspectral image analysis, high performance computing is also the urgent requirement for endmember extraction. In this paper, we investigate the fast implementation of two endmember extraction algorithms named as new simplex growing algorithm (NSGA) and kernel new simplex growing algorithm (KNSGA), which are recently developed as linear and nonlinear alternative to the SGA algorithm, respectively, and shown to be two promising endmember extraction techniques. Due to the fact that when implementing NSGA and KNSGA only the new endmember is processed at a time with the former endmembers staying the same and remaining unchanged, the time cost of simplex volume computation is reduced by simplifying matrix determinant based on the partitioned matrix determinant formula. Experimental results of simulated and real data show that the proposed fast algorithms can greatly reduce computational complexity while their performance remains invariant. [ABSTRACT FROM AUTHOR]

Nonlinear spectral mixture analysis is important when the light suffers multiple interactions among distinct materials. Few attempts have been conducted to incorporate spatial information to improve the performance of nonlinear unmixing algorithms. In this letter, local windows are adopted in the preliminary classification map to search the relevant endmembers for each pixel. Virtual endmembers, resulting from the relevant endmembers, represent the multiple-scattering effects in each pixel, and the corresponding abundances are estimated based on a modified bilinear model. Experiments on simulated and real hyperspectral images demonstrate that the proposed method provides a competitive or even better performance over some existing algorithms. [ABSTRACT FROM PUBLISHER]

In recent years, neural network-based anomaly detection methods have attracted considerable attention in the hyperspectral remote sensing domain due to their powerful reconstruction ability compared with traditional methods. However, actual probability distribution statistics hidden in the latent space are not discovered by exploiting the reconstruction error because the probability distribution of anomalies is not explicitly modeled. To address the issue, we propose a novel probability distribution representation detector (PDRD) that explores the intrinsic distribution of both the background and the anomalies for hyperspectral anomaly detection in this paper. First, we represent the hyperspectral data with multivariate Gaussian distributions from a probabilistic perspective. Then, we combine the local statistics with the obtained distributions to leverage the spatial information. Finally, the difference between the test pixel and the average expectation of the pixels in the Chebyshev neighborhood is measured by computing the modified Wasserstein distance to acquire the detection map. We conduct the experiments on three real data sets to evaluate the performance of our proposed method. The experimental results demonstrate the accuracy and efficiency of our proposed method compared to the state-of-the-art detection methods. [ABSTRACT FROM AUTHOR]

Endmember extraction is an important and challenging step to solve the spectral unmixing problem. Most existing endmember extraction algorithms (EEAs) usually find image pixels as endmembers assuming the presence of pure pixels in an image scene or generate virtual endmembers without pure-pixel assumption. When some prevalent materials have pure-pixel representation and pure pixels of other less prevalent materials are absent in the image, it would be more appropriate to extract the endmembers of both prevalent and less prevalent materials, respectively. Therefore, a novel two-stage EEA is presented in this paper. In the first stage, conventional pure-pixel-based EEAs are applied to generate a candidate pixel set, and then spatial information of the candidate pixels is exploited to determine the endmembers of prevalent materials. In the second stage, given known endmembers of prevalent materials, a modified algorithm based on nonnegative matrix factorization is performed to generate the endmembers of less prevalent materials. The validity of the proposed algorithm is demonstrated by experiments based on synthetic mixtures and a real image scene. [ABSTRACT FROM AUTHOR]

Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing. However, most of the existing BS methods fail to fully consider the interaction between spectral bands and cannot comprehensively consider the representativeness and redundancy of the selected band subset. To solve these problems, we propose an unsupervised effective band attention reconstruction framework for band selection (EBARec-BS) in this article. The framework utilizes the EBARec network to learn the representativeness of each band to the original band set and measures the redundancy between the bands by calculating the distance of each unselected band to the selected band subset. Subsequently, by designing an adaptive weight to balance the influence of the representativeness metric and redundancy metric on the band evaluation, a final band scoring function is obtained to select a band subset that well represents the original hyperspectral image and has low redundancy. Experiments on three well-known hyperspectral data sets indicate that compared with the existing BS methods, the proposed EBARec-BS is robust to noise bands and can effectively select the band subset with higher classification accuracy and less redundant information. [ABSTRACT FROM AUTHOR]

Shallow underwater topography has important practical applications in fisheries, navigation, and pipeline laying. Traditional multibeam bathymetry is limited by the high cost of largescale topographic surveys in large, shallow sand wave areas. Remote sensing inversion methods to detect shallow sand wave topography in Taiwan rely heavily on measured water depth data. To address these problems, this study proposes a largescale remote sensing inversion model of sand wave topography based on long short-term memory network machine learning. Using multi-angle sun glitter remote sensing to obtain sea surface roughness (SSR) information and by learning and training SSR and its corresponding water depth information, the sand wave topography of a largescale shallow sea sand wave region is extracted. The accuracy of the model is validated through its application to a 774 km2 area in the sand wave topography of the Taiwan Banks. The model obtains a root mean square error of 3.31–3.67 m, indicating that the method has good generalization capability and can achieve a largescale topographic understanding of shallow sand waves with some training on measured bathymetry data. Sand wave topography is widely present in tidal environments; our method has low requirements for ground data, with high application value. [ABSTRACT FROM AUTHOR]

Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it difficult to balance their effects on unmixing results. In this paper, we propose two gated autoencoder networks with the intention of adaptively controlling the contribution of spectral and spatial features in unmixing process. Gating mechanism is adopted in the networks to filter and regularize spatial features to construct an unmixing algorithm based on spectral information and supplemented by spatial information. In addition, abundance sparsity regularization and gating regularization are introduced to ensure the appropriate implementation. Experimental results validate the superiority of the proposed method to the state-of-the-art techniques in both synthetic and real-world scenes. This study confirms the effectiveness of gating mechanism in improving the accuracy and efficiency of utilizing spatial signatures for spectral unmixing. [ABSTRACT FROM AUTHOR]

Due to invariance to significant intensity differences, similarity metrics have been widely used as criteria for an area-based method for registering optical remote sensing image. However, for images with large scale and rotation difference, the robustness of similarity metrics can greatly determine the registration accuracy. In addition, area-based methods usually require appropriately selected initial values for registration parameters. This paper presents a registration approach using spatial consistency (SC) and average regional information divergence (ARID), called spatial-consistency and average regional information divergence minimization via quantum-behaved particle swarm optimization (SC-ARID-QPSO) for optical remote sensing images registration. Its key idea minimizes ARID with SC to select an ARID-minimized spatial consistent feature point set. Then, the selected consistent feature set is tuned randomly to generate a set of M registration parameters, which provide initial particle warms to implement QPSO to obtain final optimal registration parameters. The proposed ARID is used as a criterion for the selection of consistent feature set, the generation of initial parameter sets, and fitness functions used by QPSO. The iterative process of QPSO is terminated based on a custom-designed automatic stopping rule. To evaluate the performance of SC-ARID-QPSO, both simulated and real images are used for experiments for validation. In addition, two data sets are particularly designed to conduct a comparative study and analysis with existing state-of-the-art methods. The experimental results demonstrate that SC-ARID-QPSO produces better registration accuracy and robustness than compared methods. [ABSTRACT FROM AUTHOR]