Hyperspectral Change Detection Using Collaborative Sparsity and Nonlocal Low-Rank Tensor

• Published in: Jisuanji kexue yu tansuo
• Main Author: ZHAN Tianming, SONG Bo, SUN Le, WAN Minghua, YANG Guowei
• Format: Article
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2022-02-01
• Subjects: |hyperspectral|change detection|collaborative sparsity|non-local low-rank|tensor decomposition
• Online Access: http://fcst.ceaj.org/fileup/1673-9418/PDF/2009009.pdf

Hyperspectral image change detection can provide timely change information on the surface of the earth, which is essential for urban and rural planning and management. Due to the higher spectral resolution, hyperspectral images are often used to detect finer changes. Aiming at the problem of change detection by using hyperspectral image, a hyperspectral change detection method based on collaborative sparsity and nonlocal low-rank tensor is proposed. This method first obtains hyperspectral differential image at different time points, and then extracts different nonlocal similar block tensor clusters according to the nonlocal distribution characteristics of the image blocks in the differential image. Then, based on collaborative sparse regularization and low-rank regularization, a change detection model using collaborative sparsity and non-local low-rank tensor is established, and the representa-tion coefficient is obtained by solving the model using the alternating direction method of multipliers. Finally, the projection residuals of the tensor in different categories are obtained according to the representation coefficients, and then the projection residual minimization criterion is judged whether the tensor has changed. Experiments on Farm-land and Urban area in San Francisco City datasets demonstrate that the proposed method can achieve much better changes detection accuracy.