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Hyperspectral image (HIS) super-resolution aims to fuse a low-spatial-resolution HSI (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI) to obtain a high-resolution HSI (HR-HSI). Tensor-based methods have demonstrated their outstanding ability to construct the relationship between the LR-HSI and the HR-MSI. This letter introduces a nonlocal hierarchical Tucker decomposition (HTD) model for hyperspectral and multispectral image (HSI-MSI) fusion. First, similar nonlocal patch tensors are clustered according to their similarity in the HR-MSI. Next, the spatial/spectral relationship between the LR-HSI and the HR-MSI is extracted through HTD. The alternating direction method of multipliers (ADMMs) is employed to solve the proposed model. Furthermore, to overcome the high computational complexity of the model solver, we propose an efficient distributed and parallel method to accelerate the fusion process. Experimental results demonstrate that the proposed method not only substantially outperforms state-of-the-art HSI-MSI fusion methods, but also achieves a significant acceleration rate. [ABSTRACT FROM AUTHOR]

Target detection based on hyperspectral image (HSI) representations has drawn wide attention given its wide variety of features. The matrix-based approach inevitably loses spatial information and fails to explore the intrinsic multimodal structure of an HSI cube. In this letter, we propose a regularized tensor-based model without altering the data structure. We assume that an observed third-order HSI tensor is decomposed into the sum of a total variation (TV)-regularized low-rank background tensor and a sparse (TVLrS) target tensor. The two tensors are represented as the mode-3 product of a third-order tensor, called the tensor representation coefficient (TRC), and a spectra dictionary matrix. Then, the model is coined as TVLrS-TRC. The background TRC has a low-rank property, contributing to the low-rankness characterization in our model. Moreover, as the size of the background TRC term is smaller than the background tensor, characterizing its local smoothness via TV regularization reduces the computational cost compared with that of the background tensor. Extensive experiments on two real hyperspectral datasets demonstrate the advantage of the proposed method compared with the state of the art. [ABSTRACT FROM AUTHOR]

Recently, deep learning has drawn significant attention in hyperspectral image (HSI) classification. With the growth of network depth and feature integration, deep learning demands abundant labeled samples to optimize many parameters. Unfortunately, most hyperspectral data are unlabeled and the available labeled samples are extremely limited. How to obtain richer features under limited training samples is a challenge for HSI classification. To tackle this issue, a new supervised multiscale alternately updated clique network (MSCN) is proposed for HSI classification to fully employ HSI features in different scales. Based on the Clique Block, we design the multiscale alternately updated clique block (MSCB) that applies convolution kernels of various sizes to adaptively exploit the multiscale HSI information and merge them within the block. Meanwhile, the recurrent feedback architecture is introduced to reuse high-level visual information and network parameters. The proposed MSCN includes two MSCBs to capture the multiscale spectral and spatial information in turn. The MSCN improves the information flow and the efficiency of parameter tuning through the feedback mechanism and the cross-utilization of multiscale feature. It not only obtains more abstract HSI information, but also reduces the network depth and the number of parameters, thereby improving the classification accuracy under limited samples. To certify the validity of the proposed MSCN, experiments are conducted on three real HSI datasets and compared with multiple state-of-the-art deep learning-based approaches. The experimental results demonstrate that the presented multiscale network achieves superior performance, especially in the case of a small number of training samples. [ABSTRACT FROM AUTHOR]

The large data volume and high algorithm complexity of hyperspectral image (HSI) problems have posed big challenges for efficient classification of massive HSI data repositories. Recently, cloud computing architectures have become more relevant to address the big computational challenges introduced in the HSI field. This article proposes an acceleration method for HSI classification that relies on scheduling metaheuristics to automatically and optimally distribute the workload of HSI applications across multiple computing resources on a cloud platform. By analyzing the procedure of a representative classification method, we first develop its distributed and parallel implementation based on the MapReduce mechanism on Apache Spark. The subtasks of the processing flow that can be processed in a distributed way are identified as divisible tasks. The optimal execution of this application on Spark is further formulated as a divisible scheduling framework that takes into account both task execution precedences and task divisibility when allocating the divisible and indivisible subtasks onto computing nodes. The formulated scheduling framework is an optimization procedure that searches for optimized task assignments and partition counts for divisible tasks. Two metaheuristic algorithms are developed to solve this divisible scheduling problem. The scheduling results provide an optimized solution to the automatic processing of HSI big data on clouds, improving the computational efficiency of HSI classification by exploring the parallelism during the parallel processing flow. Experimental results demonstrate that our scheduling-guided approach achieves remarkable speedups by facilitating the automatic processing of HSI classification on Spark, and is scalable to the increasing HSI data volume. [ABSTRACT FROM AUTHOR]

Cloud computing has become a promising solution to efficient processing of remotely sensed big data, due to its high-performance and scalable computing capabilities. However, existing cloud solutions generally involve the problems of low resource utilization and high energy consumption when processing large-scale remote sensing datasets, affecting the quality-of-service of the cloud system. Aiming at hyperspectral image classification applications, this article proposes an energy-efficient cloud implementation by employing a multiobjective task scheduling algorithm. We first present a parallel computing mechanism for a fusion-based classification method based on Apache Spark. With the general classification flow represented by a workflow model, we formulate a multiobjective scheduling framework that jointly minimizes the total execution time as well as energy consumption. We further develop an effective scheduling algorithm to solve the multiobjective optimization problem and produce a set of Pareto-optimal solutions, providing the tradeoff between computational efficiency and energy efficiency. Experimental results demonstrate that the multiobjective scheduling approach proposed in this work can substantially reduce the execution time and energy consumption for performing large-scale hyperspectral image classification on Spark. In addition, our proposed algorithm can generate better tradeoff solutions to the multiobjective scheduling problem as compared to competing scheduling algorithms. [ABSTRACT FROM AUTHOR]

Tracking moving objects in remote-sensing videos is becoming increasingly important in remote-sensing analysis. This letter presents a novel object tracking method for remote-sensing videos. We start with using the traditional robust principal component analysis (RPCA) model to extract the moving object from the background. To describe the continuity of moving objects in spatial and temporal directions, we incorporate a 3-D total variation (3DTV) regularization into the RPCA model. Considering that the background is not static and the captured videos will contain noise because of the instability of the sensing camera, our proposed method introduces a certain part of the function to model the noise and capture the changes in background. Experimental results on real videos provided by 2016 IEEE GRSS Data Fusion Contest and 2020 Hyperspectral Object Tracking Challenge demonstrate the advantages of the moving object-tracking method via 3-D TV. [ABSTRACT FROM AUTHOR]

A spatial–spectral residual dense network-based compressive hyperspectral image (HSI) reconstruction method is proposed in this letter. The proposed method contains two networks: residual dense network for hyperspectral image reconstruction (RDNHIR) and spectral difference reconstruction network (SDRN). The RDNHIR network can extract the local features and global hierarchical features by cascading features of all residual dense blocks (RDBs). Then, SDRN takes full advantage of the strong correlation between spectral adjacent bands to better preserve the spectral feature of HSI. Finally, the adjacent spectral difference regularization is introduced into the loss function to further improve the performance. The experimental results show that the proposed method has better reconstruction quality than other state-of-the-art reconstruction methods, especially in the spectral domain. [ABSTRACT FROM AUTHOR]

Hyperspectral (HS) super-resolution, which aims at enhancing the spatial resolution of hyperspectral images (HSIs), has recently attracted considerable attention. A common way of HS super-resolution is to fuse the HSI with a higher spatial-resolution multispectral image (MSI). Various approaches have been proposed to solve this problem by establishing the degradation model of low spatial-resolution HSIs and MSIs based on matrix factorization methods, e.g., unmixing and sparse representation. However, this category of approaches cannot well construct the relationship between the high-spatial-resolution (HR) HSI and MSI. In fact, since the HSI and the MSI capture the same scene, these two image sources must have common factors. In this paper, a nonlocal tensor decomposition model for hyperspectral and multispectral image fusion (HSI-MSI fusion) is proposed. First, the nonlocal similar patch tensors of the HSI are constructed according to the MSI for the purpose of calculating the smooth order of all the patches for clustering. Then, the relationship between the HR HSI and the MSI is explored through coupled tensor canonical polyadic (CP) decomposition. The fundamental idea of the proposed model is that the factor matrices in the CP decomposition of the HR HSI’s nonlocal tensor can be shared with the matrices factorized by the MSI’s nonlocal tensor. Alternating direction method of multipliers is used to solve the proposed model. Through this method, the spatial structure of the MSI can be successfully transferred to the HSI. Experimental results on three synthetic data sets and one real data set suggest that the proposed method substantially outperforms the existing state-of-the-art HSI-MSI fusion methods. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification is an important technology for hyperspectral data analysis, which has the challenge of high dimensionality and limited training samples. Moreover, due to the spatial continuity of material distribution, it is reasonable to improve the performance of HSI classification from the local similarity viewpoint, i.e., pixels in the same local region have a high probability to share similar features. Therefore, in this paper, a novel method based on local similarity and kernel low-rank representation is proposed to improve the classification accuracy for HSI. First, taking into consideration that pixels in the same homogeneous region share similar spectral features (termed local similarity), the HSI is segmented by the superpixel segmentation algorithm to obtain the homogeneous regions. Then, a model of kernel low-rank representation based on local similarity (KLRRLS) is proposed for HSI classification. In the model of KLRRLS, the nonlinear structure characteristics of HSI are extracted by employing the kernel trick, and the low-rank constraint is used as a prior to restrict the recovery coefficient matrix in each segmentation region, thus efficiently solving the linear non-separable problem and exploiting the contextual information of HSI. Finally, the classification results are generated by fusing the result of each segmented map via the maximum voting mechanism. Experimental results on three real HSI datasets demonstrate that the proposed method can effectively improve the classification accuracy. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) is often contaminated by mixed noise, which severely affects the visual quality and subsequent applications of the data. In this paper, HSI restoration based on low-rank recovery with a local neighborhood weighted spectral–spatial total variation (TV) model is proposed, which focuses on the preservation of spatial structure and spectral fidelity. The low-rank matrix model is adopted to exploit the spectral and spatial correlation information, and the $l_{1}$ -norm is used as a prior to remove the sparse noise. Furthermore, a local spatial neighborhood weighted spectral–spatial TV is utilized to jointly model the spectral–spatial prior information; specifically, the spectral and spatial differences are both considered in the TV term, and the weight is computed by considering the local neighborhood information in the spatial domain. Alternating direction method of multipliers optimization procedure is extended to solve the presented model. Experimental results demonstrate that the proposed method can remove the mixed noise, enhance the structural information simultaneously, and offer the best performance compared with several state-of-the-art HSI restoration methods. [ABSTRACT FROM AUTHOR]

Anomaly detection plays an important role in remotely sensed hyperspectral image (HSI) processing. Recently, compressive sensing technology has been widely used in hyperspectral imaging. However, the reconstruction from compressive HSI and detection are commonly completed independently, which will reduce the processing’s efficiency and accuracy. In this paper, we propose a framework for hyperspectral compressive sensing with anomaly detection which reconstruct the HSI and detect the anomalies simultaneously. In the proposed method, the HSI is composed of the background and anomaly parts in the tensor robust principal component analysis model. To characterize the low-dimensional structure of the background, a novel tensor nuclear norm is used to constrain the background tensor. As the anomaly part is formed by a few anomalous spectra, the anomaly part is assumed to be a tuber-wise sparse tensor. In addition, to enhance the separation of the background and anomaly, we minimize the sum of Mahalanobis distance of the background pixels. Experiments on four HSIs demonstrate that the proposed method outperforms several state-of-the-art methods on both reconstruction and anomaly detection accuracies. [ABSTRACT FROM AUTHOR]

Image classification is a very important tool for remotely sensed hyperspectral image processing. Techniques able to exploit the rich spectral information contained in the data, as well as its spatial-contextual information, have shown success in recent years. Due to the high dimensionality of hyperspectral data, spectral-spatial classification techniques are quite demanding from a computational viewpoint. In this paper, we present a computationally efficient parallel implementation for a spectral-spatial classification method based on spatially adaptive Markov random fields (MRFs). The method learns the spectral information from a sparse multinomial logistic regression classifier, and the spatial information is characterized by modeling the potential function associated with a weighted MRF as a spatially adaptive vector total variation function. The parallel implementation has been carried out using commodity graphics processing units (GPUs) and the NVIDIA's Compute Unified Device Architecture. It optimizes the work allocation and input/output transfers between the central processing unit and the GPU, taking full advantages of the computational power of GPUs as well as the high bandwidth and low latency of shared memory. As a result, the algorithm exploits the massively parallel nature of GPUs to achieve significant acceleration factors (higher than 70x) with regards to the serial and multicore versions of the same classifier on an NVIDIA Tesla K20C platform. [ABSTRACT FROM PUBLISHER]

Target detection plays an important role in the field of hyperspectral image (HSI) remote sensing. In this letter, a novel matched subspace detector based on low-rank regularized least squares (LRLS-MSD) is proposed for hyperspectral target detection. As pixels in an HSI have global correlation and can be represented in subspace, the low-rank regularization is introduced in the least squares model. An effective algorithm is presented to solve the problem. Then, the detection results are generated according to the generalized likelihood ratio test with statistical hypotheses. The experimental results suggest an advantage of the low-rank regularization over other classical target detection methods. [ABSTRACT FROM PUBLISHER]

Cloud computing offers the possibility to store and process massive amounts of remotely sensed hyperspectral data in a distributed way. Dimensionality reduction is an important task in hyperspectral imaging, as hyperspectral data often contains redundancy that can be removed prior to analysis of the data in repositories. In this regard, the development of dimensionality reduction techniques in cloud computing environments can provide both efficient storage and preprocessing of the data. In this paper, we develop a parallel and distributed implementation of a widely used technique for hyperspectral dimensionality reduction: principal component analysis (PCA), based on cloud computing architectures. Our implementation utilizes Hadoop’s distributed file system (HDFS) to realize distributed storage, uses Apache Spark as the computing engine, and is developed based on the map-reduce parallel model, taking full advantage of the high throughput access and high performance distributed computing capabilities of cloud computing environments. We first optimized the traditional PCA algorithm to be well suited for parallel and distributed computing, and then we implemented it on a real cloud computing architecture. Our experimental results, conducted using several hyperspectral datasets, reveal very high performance for the proposed distributed parallel method. [ABSTRACT FROM PUBLISHER]

A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on low-rank and sparse representation. The proposed method is based on the separation of the background and the anomalies in the observed data. Since each pixel in the background can be approximately represented by a background dictionary and the representation coefficients of all pixels form a low-rank matrix, a low-rank representation is used to model the background part. To better characterize each pixel's local representation, a sparsity-inducing regularization term is added to the representation coefficients. Moreover, a dictionary construction strategy is adopted to make the dictionary more stable and discriminative. Then, the anomalies are determined by the response of the residual matrix. An important advantage of the proposed algorithm is that it combines the global and local structure in the HSI. Experimental results have been conducted using both simulated and real data sets. These experiments indicate that our algorithm achieves very promising anomaly detection performance. [ABSTRACT FROM PUBLISHER]

In this letter, we present an efficient parallel implementation of composite kernels in support vector machines (SVMs) for hyperspectral image (HSI) classification. Our implementation makes effective use of commodity graphics processing units (GPUs). Specifically, we port the calculation of composite kernels to GPUs, perform intensive computations based on NVidia's compute unified device architecture, and execute the rest of the operations related with control and small data calculations in the CPU. Our experimental results, conducted using real hyperspectral data sets and NVidia GPU platforms, indicate significant improvements in terms of computational effectiveness, achieving near-real-time performance of spatial–spectral HSI classification for the first time in the literature. [ABSTRACT FROM PUBLISHER]

Classification is one of the most important analysis techniques for hyperspectral image analysis. Sparse representation is an extremely powerful tool for this purpose, but the high computational complexity of sparse representation-based classification techniques limits their application in time-critical scenarios. To improve the efficiency and performance of sparse representation classification techniques for hyperspectral image analysis, this paper develops a new parallel implementation on graphics processing units (GPUs). First, an optimized sparse representation model based on spatial correlation regularization and a spectral fidelity term is introduced. Then, we use this approach as a case study to illustrate the advantages and potential challenges of applying GPU parallel optimization principles to the considered problem. The first GPU optimization algorithm for sparse representation classification (SRCSC_P) of hyperspectral images is proposed in this paper, and a parallel implementation of the proposed method is developed using compute unified device architecture (CUDA) on GPUs. The GPU parallel implementation is compared with the serial and multicore implementations on CPUs. Experimental results based on real hyperspectral datasets show that the average speedup of SRCSC_P is more than \mathbf130 \times, and the proposed approach is able to provide results accurately and fast, which is appealing for computationally efficient hyperspectral data processing. [ABSTRACT FROM PUBLISHER]

Spatial–spectral classification is a very important topic in the field of remotely sensed hyperspectral imaging. In this work, we develop a parallel implementation of a novel supervised spectral–spatial classifier, which models the likelihood probability via \bml\mathbf{1} - \bm{l\mathbf{2}} sparse representation and the spatial prior as a Gibbs distribution. This classifier takes advantage of the spatial piecewise smoothness and correlation of neighboring pixels in the spatial domain, but its computational complexity is very high which makes its application to time-critical scenarios quite limited. In order to improve the computational efficiency of the algorithm, we optimized its serial version and developed a parallel implementation for commodity graphics processing units (GPUs). Our parallel spatial–spectral classifier with sparse representation and Markov random fields (SSC-SRMRF-P) exploits the low-level architecture of GPUs. The parallel optimization of the proposed method has been carried out using the compute unified device architecture (CUDA). The performance of the parallel implementation is evaluated and compared with the serial and multicore implementations on central processing units (CPUs). In fact, the proposed method has been designed to adequately exploit the massive data parallel capacities of GPUs together with the control and logic capacities of CPUs, thus resorting to a heterogeneous CPU–GPU framework in the design of the parallel algorithm. Experimental results using real hyperpsectral images demonstrate very high performance for the proposed CPU–GPU parallel method, both in terms of classification accuracy and computational performance. [ABSTRACT FROM PUBLISHER]

This letter presents a postprocessing algorithm for a kernel sparse representation (KSR)-based hyperspectral image classifier, which is based on the integration of spatial and spectral information. A pixelwise KSR is first used to find the sparse coefficient vectors of the hyperspectral image. Then, a sparsity concentration index (SCI) rule-guided semilocal spatial graph regularization (SSG), called SSG+SCI, is proposed to determine refined sparse coefficient vectors that promote spatial continuity within each class. Finally, these refined coefficient vectors are used to obtain the final classification map. Compared with previous approaches based on similar spatial–spectral postprocessing strategies, SSG+SCI clearly outperforms their results in terms of accuracy and the number of training samples, as it is demonstrated with two real hyperspectral images. [ABSTRACT FROM PUBLISHER]