Zebin Wu

Spatial-Spectral Kernel Sparse Representation for Hyperspectral Image Classification

Kernel sparse representation classification (KSRC), a nonlinear extension of sparse representation classification, shows its good performance for hyperspectral image classification. However, KSRC only considers the spectra of unordered pixels, without incorporating information on the spatially adjacent data. This paper proposes a neighboring filtering kernel to spatial-spectral kernel sparse representation for enhanced classification of hyperspectral images. The novelty of this work consists in: 1) presenting a framework of spatial-spectral KSRC; and 2) measuring the spatial similarity by means of neighborhood filtering in the kernel feature space. Experiments on several hyperspectral images demonstrate the effectiveness of the presented method, and the proposed neighboring filtering kernel outperforms the existing spatial-spectral kernels. In addition, the proposed spatial-spectral KSRC opens a wide field for future developments in which filtering methods can be easily incorporated.

Supervised Spectral–Spatial Hyperspectral Image Classification With Weighted Markov Random Fields

This paper presents a new approach for hyperspectral image classification exploiting spectral-spatial information. Under the maximum a posteriori framework, we propose a supervised classification model which includes a spectral data fidelity term and a spatially adaptive Markov random field (MRF) prior in the hidden field. The data fidelity term adopted in this paper is learned from the sparse multinomial logistic regression (SMLR) classifier, while the spatially adaptive MRF prior is modeled by a spatially adaptive total variation (SpATV) regularization to enforce a spatially smooth classifier. To further improve the classification accuracy, the true labels of training samples are fixed as an additional constraint in the proposed model. Thus, our model takes full advantage of exploiting the spatial and contextual information present in the hyperspectral image. An efficient hyperspectral image classification algorithm, named SMLR-SpATV, is then developed to solve the final proposed model using the alternating direction method of multipliers. Experimental results on real hyperspectral data sets demonstrate that the proposed approach outperforms many state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic.

Probabilistic-Kernel Collaborative Representation for Spatial–Spectral Hyperspectral Image Classification

This paper presents a new approach for accurate spatial-spectral classification of hyperspectral images, which consists of three main steps. First, a pixelwise classifier, i.e., the probabilistic-kernel collaborative representation classification (PKCRC), is proposed to obtain a set of classification probability maps using the spectral information contained in the original data. This is achieved by means of a kernel extension based on collaborative representation (CR) classification. Then, an adaptive weighted graph (AWG)-based postprocessing model is utilized to include the spatial information by refining the obtained pixelwise probability maps. Furthermore, to deal with scenarios dominated by limited training samples, we modify the postprocessing model by fixing the probabilistic outputs of training samples to integrate the spatial and label information. The proposed approach is able to cover different analysis scenarios by means of a fully adaptive processing chain (based on three steps) for hyperspectral image classification. All the techniques that integrate the proposed approach have a closed-form analytic solution and are easy to be implemented and calculated, exhibiting potential benefits for hyperspectral image classification under different conditions. Specifically, the proposed method is experimentally evaluated using two real hyperspectral imagery data sets, exhibiting good classification performance even when the number of training samples available a priori is very limited.

Parallel and Distributed Dimensionality Reduction of Hyperspectral Data on Cloud Computing Architectures

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 Hadoops 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.

Parallel Implementation of Sparse Representation Classifiers for Hyperspectral Imagery on GPUs

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 130×, and the proposed approach is able to provide results accurately and fast, which is appealing for computationally efficient hyperspectral data processing.

Hyperspectral Image Classification Using Kernel Sparse Representation and Semilocal Spatial Graph Regularization

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.

Parallel Spatial–Spectral Hyperspectral Image Classification With Sparse Representation and Markov Random Fields on GPUs

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 l1 - l2 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 hyperspectral images demonstrate very high performance for the proposed CPU-GPU parallel method, both in terms of classification accuracy and computational performance.

A novel l1/2 sparse regression method for hyperspectral unmixing

Hyperspectral unmixing (HU) is a popular tool in remotely sensed hyperspectral data interpretation, and it is used to estimate the number of reference spectra (end-members), their spectral signatures, and their fractional abundances. However, it can also be assumed that the observed image signatures can be expressed in the form of linear combinations of a large number of pure spectral signatures known in advance (e.g. spectra collected on the ground by a field spectro-radiometer, called a spectral library). Under this assumption, the solution of the fractional abundances of each spectrum can be seen as sparse, and the HU problem can be modelled as a constrained sparse regression (CSR) problem used to compute the fractional abundances in a sparse (i.e. with a small number of terms) linear mixture of spectra, selected from large libraries. In this article, we use the l 1/2 regularizer with the properties of unbiasedness and sparsity to enforce the sparsity of the fractional abundances instead of the l 0 and l 1 regularizers in CSR unmixing models, as the l 1/2 regularizer is much easier to be solved than the l 0 regularizer and has stronger sparsity than the l 1 regularizer (Xu et al. 2010). A reweighted iterative algorithm is introduced to convert the l 1/2 problem into the l 1 problem; we then use the Split Bregman iterative algorithm to solve this reweighted l 1 problem by a linear transformation. The experiments on simulated and real data both show that the l 1/2 regularized sparse regression method is effective and accurate on linear hyperspectral unmixing.

Spectral–Spatial Classification of Hyperspectral Image Based on Low-Rank Decomposition

Spectral–spatial classification methods have been proven to be effective in hyperspectral image (HSI) classification. However, most of the methods make use of the correlation in a small neighborhood. In this paper, a novel low-rank decomposition spectral–spatial method (LRDSS) is proposed. LRDSS incorporates the global and local correlation where the global correlation is introduced by discovering the low-dimensional structure in the high-dimensional data, and local correlation is modeled by Markov Random Field (MRF). Specifically, all pixels’ spectrums in a homogeneous area are assumed to have low-dimensional structure. Low rankness is a fine property to characterize the low-dimensional structure and robust principal component analysis (RPCA) is used to extract the low-rank data. Then, the spectral information is obtained by the probabilistic support vector machine (SVM) classifier applied on the low-rank data. Moreover, the MRF models local correlation by encouraging neighboring pixels taking the same label. The maximum a posterior classification is computed by min-cut-based optimization algorithm. The experimental results suggest that LRDSS outperforms the other spectral–spatial classification methods investigated in this paper in terms of classification accuracies.

Real-Time Implementation of the Sparse Multinomial Logistic Regression for Hyperspectral Image Classification on GPUs

In this letter, a real-time implementation of the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm for sparse multinomial logistic regression is presented on commodity graphics processing units (GPUs) using Nvidias compute unified device architecture. The proposed parallel method properly exploits the GPU architecture at the low level, including its shared memory, and takes full advantage of the computational power of GPUs to achieve real-time classification performance of hyperspectral images for the first time in the hyperspectral imaging literature. Our experimental results reveal remarkable acceleration factors and real-time performance, while retaining exactly the same classification accuracy with regard to the serial and multicore versions of the classifier.