Tensor singular spectral analysis for 3D feature extraction in hyperspectral images.

Fu, Hang; Sun, Genyun; Zhang, Aizhu; Shao, Baojie; Ren, Jinchang; Jia, Xiuping

Abstract

Due to the cubic structure of a hyperspectral image (HSI), how to characterize its spectral and spatial properties in three dimensions is challenging. Conventional spectral-spatial methods usually extract spectral and spatial information separately, ignoring their intrinsic correlations. Recently, some 3D feature extraction methods are developed for the extraction of spectral and spatial features simultaneously, although they rely on local spatial-spectral regions and thus ignore the global spectral similarity and spatial consistency. Meanwhile, some of these methods contain huge model parameters which require a large number of training samples. In this paper, a novel Tensor Singular Spectral Analysis (TensorSSA) method is proposed to extract global and low-rank features of HSI. In TensorSSA, an adaptive embedding operation is first proposed to construct a trajectory tensor corresponding to the entire HSI, which takes full advantage of the spatial similarity and improves the adequate representation of the global low-rank properties of the HSI. Moreover, the obtained trajectory tensor, which contains the global and local spatial and spectral information of the HSI, is decomposed by the Tensor singular value decomposition (t-SVD) to explore its low-rank intrinsic features. Finally, the efficacy of the extracted features is evaluated using the accuracy of image classification with a support vector machine (SVM) classifier. Experimental results on three publicly available datasets have fully demonstrated the superiority of the proposed TensorSSA over a few state-of-the-art 2D/3D feature extraction and deep learning algorithms, even with a limited number of training samples.

Citation

FU, H., SUN, G., ZHANG, A., SHAO, B., REN, J. and JIA, X. 2023. Tensor singular spectral analysis for 3D feature extraction in hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 61, article 5403914. Available from: https://doi.org/10.1109/TGRS.2023.3272669

• Journal Article Type: Article
• Acceptance Date: Dec 31, 2022
• Online Publication Date: May 9, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: May 25, 2023
• Publicly Available Date: May 25, 2023
• Journal: IEEE transactions on geoscience and remote sensing
• Print ISSN: 0196-2892
• Electronic ISSN: 1558-0644
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 61
• Article Number: 5403914
• DOI: https://doi.org/10.1109/TGRS.2023.3272669
• Keywords: Hyperspectral image (HSI); 3D feature extraction; TensorSSA; Adaptive embedding; Trajectory tensor
• Public URL: https://rgu-repository.worktribe.com/output/1961793