Low-Rank and Sparse Representation for Hyperspectral Image Processing: A Review

As hyperspectral imagery (HSI) contains rich spectral and spatial information, a novel principal component analysis (PCA) and segmented-PCA (SPCA)-based multiscale 2-D-singular spectrum analysis (2-D-SSA) fusion method is proposed for joint spectral-spatial HSI feature extraction and classification. Considering the overall spectra and adjacent band correlations of objects, the PCA and SPCA methods are utilized first for spectral dimension reduction, respectively. Then, multiscale 2-D-SSA is applied onto the SPCA dimension-reduced images to extract abundant spatial features at different scales, where PCA is applied again for dimensionality reduction. The obtained multiscale spatial features are then fused with the global spectral features derived from PCA to form multiscale spectral-spatial features (MSF-PCs). The performance of the extracted MSF-PCs is evaluated using the support vector machine (SVM) classifier. Experiments on four benchmark HSI data sets have shown that the proposed method outperforms other state-of-the-art feature extraction methods, including several deep learning approaches, when only a small number of training samples are available.

Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery

Deep learning methods have shown considerable potential for hyperspectral image (HSI) classification, which can achieve high accuracy compared with traditional methods. However, they often need a large number of training samples and have a lot of parameters and high computational overhead. To solve these problems, this article proposes new network architecture, LiteDepthwiseNet, for HSI classification. Based on 3-D depthwise convolution, LiteDepthwiseNet can decompose standard convolution into depthwise convolution and pointwise convolution, which can achieve high classification performance with minimal parameters. Moreover, we remove the ReLU layer and batch normalization layer in the original 3-D depthwise convolution, which is likely to improve the overfitting phenomenon of the model on small-sized data sets. In addition, focal loss is used as the loss function to improve the model’s attention on difficult samples and unbalanced data, and its training performance is significantly better than that of cross-entropy loss or balanced cross-entropy loss. Experiment results on five benchmark hyperspectral data sets show that LiteDepthwiseNet achieves state-of-the-art performance with a very small number of parameters and low computational cost. 

LiteDepthwiseNet: A Lightweight Network for Hyperspectral Image Classification

Singular spectral analysis (SSA) has recently been successfully applied to feature extraction in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D SSA in spatial domain. However, there are some drawbacks, such as sensitivity to the window size, high computational complexity under a large window, and failing to extract joint spectral-spatial features. To tackle these issues, in this article, we propose superpixelwise adaptive SSA (SpaSSA), that is superpixelwise adaptive SSA for exploiting local spatial information of HSI. The extraction of local (instead of global) features, particularly in HSI, can be more effective for characterizing the objects within an image. In SpaSSA, conventional SSA and 2-D SSA are combined and adaptively applied to each superpixel derived from an oversegmented HSI. According to the size of the derived superpixels, either SSA or 2-D singular spectrum analysis (2D-SSA) is adaptively applied for feature extraction, where the embedding window in 2D-SSA is also adaptive to the size of the superpixel. Experimental results on the three datasets have shown that the proposed SpaSSA outperforms both SSA and 2D-SSA in terms of classification accuracy and computational complexity. By combining SpaSSA with the principal component analysis (SpaSSA-PCA), the accuracy of land-cover analysis can be further improved, outperforming several state-of-the-art approaches.

/pdf/spassa-superpixelwise-adaptive-ssa-for-unsupervised-spatial-4wf6goz5an.pdf

SpaSSA: Superpixelwise Adaptive SSA for Unsupervised Spatial-Spectral Feature Extraction in Hyperspectral Image.

Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a more comprehensive characterization of the Earth&#x2019;s surface. To better exploit HSIs, a large number of algorithms have been developed during the past few decades. Due to their very high correlation between spectral channels and spatial pixels, HSIs have intrinsically sparse and low-rank structures. The sparse representation (SR) and low-rank representation (LRR)-based methods have proven to be powerful tools for HSI processing and are widely used in different HS fields. In this article, we present a survey of low-rank and sparse-based HSI processing methods in the fields of denoising, superresolution, dimension reduction, unmixing, classification, and anomaly detection. The purpose is to provide guidelines and inspiration to practitioners for promoting the development of HSI processing. For a listing of the key terms discussed in this article, see &#x201C;Nomenclature.&#x201D 

Low-Rank and Sparse Representation for Hyperspectral Image Processing: A review

Local adaptive joint sparse representation for hyperspectral image classification

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Local adaptive joint sparse representation for hyperspectral image classification