Zhihui Wei

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.

A New Pan-Sharpening Method With Deep Neural Networks

A deep neural network (DNN)-based new pansharpening method for the remote sensing image fusion problem is proposed in this letter. Research on representation learning suggests that the DNN can effectively model complex relationships between variables via the composition of several levels of nonlinearity. Inspired by this observation, a modified sparse denoising autoencoder (MSDA) algorithm is proposed to train the relationship between high-resolution (HR) and low-resolution (LR) image patches, which can be represented by the DNN. The HR/LR image patches only sample from the HR/LR panchromatic (PAN) images at hand, respectively, without requiring other training images. By connecting a series of MSDAs, we obtain a stacked MSDA (S-MSDA), which can effectively pretrain the DNN. Moreover, in order to better train the DNN, the entire DNN is again trained by a back-propagation algorithm after pretraining. Finally, assuming that the relationship between HR/LR multispectral (MS) image patches is the same as that between HR/LR PAN image patches, the HR MS image will be reconstructed from the observed LR MS image using the trained DNN. Comparative experimental results with several quality assessment indexes show that the proposed method outperforms other pan-sharpening methods in terms of visual perception and numerical measures.

Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation

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 pixels 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.

Variational Bayesian Method for Retinex

In this paper, we propose a variational Bayesian method for Retinex to simulate and interpret how the human visual system perceives color. To construct a hierarchical Bayesian model, we use the Gibbs distributions as prior distributions for the reflectance and the illumination, and the gamma distributions for the model parameters. By assuming that the reflection function is piecewise continuous and illumination function is spatially smooth, we define the energy functions in the Gibbs distributions as a total variation function and a smooth function for the reflectance and the illumination, respectively. We then apply the variational Bayes approximation to obtain the approximation of the posterior distribution of unknowns so that the unknown images and hyperparameters are estimated simultaneously. Experimental results demonstrate the efficiency of the proposed method for providing competitive performance without additional information about the unknown parameters, and when prior information is added the proposed method outperforms the non-Bayesian-based Retinex methods we compared.

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.

Perceptual image quality assessment based on structural similarity and visual masking

We propose an improved objective image quality assessment method based on the structural similarity and visual masking, called the Perceptual Image Quality Assessment (PIQA). The PIQA contains three similarity measures: the luminance comparison measure, the structure comparison measure, the contrast comparison measure as same as the Structure Similarity (SSIM) and its variants. Firstly, in order to improve the ability of distinguishing the structure information in blurred images and noisy images, we modify the structure comparison measure by using the improved structure tensor which is more efficient for describing the structure information in global areas. Secondly, based on the perceptual characters of Human Visual System (HVS) perceptual process, the contrast masking and neighborhood masking are integrated to the contrast comparison measure. Finally, three measures are pooled together to compute the PIQA metric. Comparing with the state-of-the-art methods including Multi-scale SSIM (MS-SSIM), Visual Signal to Noise Ratio (VSNR) and Visual Information Fidelity (VIF) criterion, simulation results show that our approach is highly consistent with HVS perceptual process, and also delivers better performance.

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.

New image restoration method associated with tetrolets shrinkage and weighted anisotropic total variation

Image restoration is one of the most classical problems in image processing. The main issues of image restoration are deblurring, denoising and preserving fine details. In order to obtain good restored images, we propose a new image restoration method based on a compound regularization model associated with the weighted anisotropic total variation (WATV) and the tetrolets-based sparsity. The WATV recovers sharp edges by embedding two directional gradient operators into the original anisotropic total variation (ATV), and the tetrolet transform adapts its basis to the local image structures. Thus, our model can preserve details such as textures and edges in the processing of image restoration by combining the WATV with the tetrolets-based sparsity. We present an alternate iterative scheme which consists of the variable splitting method and the operator splitting method to solve the proposed minimization problem. Experimental results demonstrate the efficiency of our image restoration method for preserving the structure details and the sharp edges of image.