Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Recently, the superpixel segmentation is introduced into the hyperspectral image (HSI) classification to exploit the spatial information. However, the size of superpixel is hard to determine since small superpixels lack enough spatial information and large superpixels usually result in error segmentation. Therefore, a multiscale superpixels based sparse representation (MSSR) algorithm is proposed to utilize the spatial-spectral information of multiscale superpixels for the HSI classification. Specifically, multiscale superpixels of a HSI are generated firstly. Then, the joint sparse representation classification (JSRC) is used to obtain the class labels of superpixels of different scales. Finally, the majority voting is applied on the labels of different scales to create the final class label for each pixel. Experimental results show that the proposed MSSR algorithm outperforms several well-known classification algorithms.

Recently, superpixel segmentation has been widely employed in hyperspectral image (HSI) classification of remote sensing. However, the structures of land-covers in HSI commonly vary greatly, which makes it difficult to fully fit the boundaries of land-covers by single-scale superpixel segmentation. Moreover, the shape-irregularity of superpixel brings challenge for depth feature extraction. To overcome these issues, a multiscale superpixel depth feature extraction (MSDFE) method is proposed for HSI classification in this article, which effectively explores and integrates the spatial-spectral information of land-covers by adopting multiscale superpixel segmentation, constructing statistical features of superpixel, and conducting depth feature extraction. Specifically, to exploit rich spatial information of HSI, multiscale superpixel segmentation is firstly applied on the HSI. Once superpixels on different scales are obtained, two-dimensional statistical features with a united form are constructed for these superpixels with different spatial shapes. Based on these two-dimensional statistical features, a convolutional neural network is utilized to learn deeper features and classify these depth features. Finally, an adaptive strategy is adopted to fuse the multiscale classification results. Experiments on three real hyperspectral datasets indicate the superiority of the proposed MSDFE method over several state-of-the-art methods.

In this article, a region-division-based joint sparse representation classification (RDJSRC) method is proposed to solve the heterogeneous region problem in the joint sparse representation classification (JSRC) method used in hyperspectral image (HSI) classification. The RDJSRC method incorporates regional information, obtained by the hidden Markov random field (HMRF), into the JSRC to reduce the interference of heterogeneous pixels in the neighbourhood of the test pixel and finally improve the classification performance. The framework of this method is as follows. The first several principal components (PCs) are initially selected to be the new HSI by transforming the original HSI with the PC analysis algorithm. Then, the regional information containing the spatial structure of the HSI is obtained by applying the HMRF algorithm to the first PC. Through incorporating this regional information into the JSRC procedure, the initial label of the test pixel can be jointly determined by the new HSI pixels within the homogeneity in the search window. Ultimately, the final label of the test pixel is determined by a voting strategy based on multiple classification results. Compared with several classification methods, experimental results, indicate that this method achieves improvement from 2 to 3% in HSI classification.

Hyperspectral remotely sensed images contain not only the spatial information of ground objects, but also their rich spectral information. Effectively improve the classification accuracy of hyperspectral images (HSIs), the purpose is to accurately grasp the current land resource utilization information, which is of great significance to the formulation and implementation of future land and space planning. Existing research on the classification of hyperspectral remotely sensed images uses a single-scale superpixel method for image segmentation. The optimal number of superpixels cannot be determined, image details may get missed, and a single kernel matrix cannot represent the information from multiple features, which results in reduced classification accuracy. Therefore, this study intends to use the superpixel segmentation method to perform multiscale superpixel segmentation on the principal components of HSIs at multiple scales, and to couple the multiscale superpixel spatial spectral kernel (SSK) with the original spectral kernel to form a synthetic kernel using weights for HSI classification. The three HSIs of Pavia University, Pavia Center, and Washington DC Mall are used as the experimental data to test and analyze this method. The experimental results show that the effective classification accuracies for the three datasets obtained by this method are 5.28%, 5.90%, and 7.71% higher than the five comparison methods, at best. The results prove that this method can effectively solve the problem of imprecise image feature extraction and an unknown number of initial superpixels and can significantly improve the classification accuracy of HSIs.

Extracting spectral-spatial information via sparse representation is a hotspot for hyperspectral image (HSI) classification. However, the spectral-spatial information extracted by the traditional joint sparse representation classification (JSRC) method is affected by heterogeneous and noisy pixels, which leads to some misclassifications. In this paper, we proposed a spectral-spatial classification framework based on joint superpixel-constrained and weighted sparse representation for HSI classification. Superpixel constraint is firstly used to remove the effects of heterogeneous pixels, which are located in a fixed sized blocks adopted by JSRC. The weighting scheme is then conducted to suppress the effects of noise. Finally, JSRC is performed on the superpixel-constrained and weighted regions obtained from the first two steps. The experimental results on Indian Pines dataset indicate that the proposed method outperforms compared methods, with more effective and robust performance.

In virtue of the spatial structural characteristic of surface materials, the performance of the hyperspectral image classification can be boosted by incorporating texture information. Normally, the spatial structure can be extracted by predefined operators, including the popular extended multiattribute profiles (EMAPs) and the Gabor filters. Recently, superpixel segmentation, which reflects the homogeneous regularity of objects, has drawn much attention in the field. In this paper, a collaborative representation-based multiscale superpixel fusion (CRMSF) approach has been proposed for the hyperspectral image classification. First, after obtaining the EMAPs from the raw hyperspectral image, a group of predesigned 3-D Gabor wavelet filters is convolved with the EMAP features, and the EMAP-Gabor features can, thus, be achieved. Second, the collaborative representation-based classification (CRC) is employed to fully and efficiently make use of the huge amount of extracted EMAP-Gabor features. Third, multiscale superpixel maps are generated from the EMAP features that are utilized to regularize the classification map obtained by CRC. A heuristic strategy has been specially devised to automatically decide the number of extracted superpixels in multiple scales, which can be perfectly compatible with hyperspectral images having various spatial sizes and spatial resolutions. This is the most important contribution of the developed CRMSF approach. Finally, the classification task is accomplished by fusing the multiple regularized classification maps. The CRMSF approach has been evaluated on four popular hyperspectral image data sets, and the experimental results show the advantages of CRMSF, particularly for a hyperspectral image with high spatial resolution.

Classification of hyperspectral images via weighted spatial correlation representation

In recent studies, superpixel segmentation has been integrated into hyperspectral (HS) image classification methods. However, the existing superpixel-based classification methods usually suffer from two serious problems. First, the accuracy and efficiency of current superpixel segmentation approaches cannot meet the demands of practical applications for HS images; second, conventional superpixel-based classification methods generally consider each generated superpixel as a unit for the image classification, which may help to reduce the computing time but result in a significant decrease of the classification accuracy. To solve the problems, we propose a fast region growing based superpixel segmentation (FRGSS) algorithm and a novel texture-adaptive superpixel integration strategy (TASIS) for the HS image classification. Experimental results on real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) HS images demonstrate that the proposed FRGSS outperforms the state-of-the-art superpixel algorithm. In addition, the superiority of the TASIS is verified compared to the pixel-wise and the conventional superpixel-based classification methods.

Hyperspectral image classification based on discriminative locality preserving broad learning system

Joint sparse representation classification (JSRC) is a representative spectral–spatial classifier for hyperspectral images (HSIs). However, the JSRC is inappropriate for highly heterogeneous areas due to the spatial information being extracted from a fixed-sized neighborhood block, which is often unable to conform to the naturally irregular structure of land cover. To address this problem, a superpixel-based JSRC with nonlocal weighting, i.e., superpixel-based nonlocal weighted JSRC (SNLW-JSRC), is proposed in this paper. In SNLW-JSRC, the superpixel representation of an HSI is first constructed based on an entropy rate segmentation method. This strategy forms homogeneous neighborhoods with naturally irregular structures and alleviates the inclusion of pixels from different classes in the process of spatial information extraction. Afterwards, the superpixel-based nonlocal weighting (SNLW) scheme is built to weigh the superpixel based on its structural and spectral information. In this way, the weight of one specific neighboring pixel is determined by the local structural similarity between the neighboring pixel and the central test pixel. Then, the obtained local weights are used to generate the weighted mean data for each superpixel. Finally, JSRC is used to produce the superpixel-level classification. This speeds up the sparse representation and makes the spatial content more centralized and compact. To verify the proposed SNLW-JSRC method, we conducted experiments on four benchmark hyperspectral datasets, namely Indian Pines, Pavia University, Salinas, and DFC2013. The experimental results suggest that the SNLW-JSRC can achieve better classification results than the other four SRC-based algorithms and the classical support vector machine algorithm. Moreover, the SNLW-JSRC can also outperform the other SRC-based algorithms, even with a small number of training samples.

By means of a sparse collaborative representation mechanism, sparse-representation-based classifiers show a superior performance in hyperspectral image (HSI) classification. Exploiting the similarity and distinctiveness of HSI neighboring pixels, we propose a new nearest regularized joint sparse representation (NRJSR) classification method in this letter. In the classification process of the central test pixel, the weights of different neighboring pixels and the sparse representation coefficients of different training samples are optimized simultaneously within a regularized sparsity model, which can obtain adaptive weights with good joint sparse representation ability. An alternative iteration strategy is used to solve the regularized joint sparsity model. The proposed NRJSR algorithm is tested on two benchmark HSI data sets. Experimental results demonstrate that the proposed algorithm performs better than other sparsity-based algorithms and spectral and spectral–spatial support vector machine classifiers.

Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold. However, a specific characteristic of HSIs, i.e., irregular spatial dependency, is not taken into consideration in the method design, which can yield many spatially homogenous subregions in an HSI scence. Conventional manifold learning methods, such as a locality preserving projection (LPP), pursue a unified projection on the entire HSI, while neglecting the local homogeneities on the HSI manifold caused by those spatially homogenous subregions. In this work, we propose a novel multiscale superpixelwise LPP (MSuperLPP) for HSI classification to overcome the challenge. First, we partition an HSI into homogeneous subregions with a multiscale superpixel segmentation. Then, on each scale, subregion specific LPPs and the associated preliminary classifications are performed. Finally, we aggregate the classification results from all scales using a decision fusion strategy to achieve the final result. Experimental results on three real hyperspectral data sets validate the effectiveness of our method.

Discriminant sub-dictionary learning with adaptive multiscale superpixel representation for hyperspectral image classification

Hyperspectral image (HSI) segmentation and classification is trending research in military and civil applications area.However, HSI classification is facing various challenges in analyzing spectral and spatial regions.In order to improve the performance of HSI classification models, segmentation is essential step.Therefore, this article is focused on implementation of unified HSI segmentation network (HSIS-Net) using active learning.Initially, HSI preprocessing operation is performed to normalize the spectral-spatial regions.Then, joint spatial-spectral boundary extraction operation is performed using spatial information divergence (SID) and spectral correlation mapper (SCM).Finally, segmentation of boundary estimated HSI bands is performed using multi-view active learning network based fully convolutional segmentation network (MAL-FCSN).The simulations revealed that the proposed HSIS-Net resulted in superior segmentation performance with segmentation accuracy (SA) of 0.999, and segmentation F1-score of 0.999 as compared to the existing HSI classification approaches for four publicly available HSI datasets.

Convolutional Neural Networks (CNNs) have been extensively applied to hyperspectral (HS) image classification tasks and achieved promising performance. However, for CNN based HS image classification methods, it is hard to depict the dependencies among HS image pixels in long-range distanced positions and bands. Moreover, the limited receptive field of the convolutional layers extremely hinders the development of the CNN structure. To tackle these problems, in this paper, the novel Bottleneck Spatial-Spectral Transformer (BS2T) is proposed to depict the long-range global dependencies of HS image pixels, which can be regarded as a feature extraction module for HS image classification networks. More specifically, inspired by Bottleneck Transformer in computer vision, for HS image feature extraction, the proposed BS2T is incorporated with a feature contraction module, a multi-head spatial-spectral self-attention (MHS2A) module and a feature expansion module. In this way, convolutional operations are replaced by the MHS2A to capture the long-range dependency of HS pixels regardless of their spatial position and distance. Meanwhile, in the MHS2A module, to highlight the spectral features of HS images, we introduce the spectral information and content spatial positional information to classical multi-head self-attentions to make the attentions more positional aware and spectral aware. On this basis, a dual-branch HS image classification framework based on 3D CNN and BS2T is defined for jointly extracting the local-global features of HS images. Experimental results on three public HS image classification datasets show that the proposed classification framework achieves a significant improvement when comparing with the state-of-the-art methods. The source code of the proposed framework can be downloaded from https://github.com/srxlnnu/BS2T.