Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 13; pp. 1174 - 1188
• Main Authors: Sun, Le, Wu, Feiyang, Zhan, Tianming, Liu, Wei, Wang, Jin, Jeon, Byeungwoo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Coexistence; Collaboration; Correlation; Decomposition; Hyperspectral imaging; Image processing; Libraries; Low-rank; Mathematical analysis; nonlocal similarity; Patches (structures); Pixels; Regularization; sparse unmixing; Spatial data; Spatial resolution; Spectral correlation; tensor decomposition; Tensors
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2020.2980576

The low spatial resolution of hyperspectral images leads to the coexistence of multiple ground objects in a single pixel (called mixed pixels). A large number of mixed pixels in a hyperspectral image hinders the subsequent analysis and application of the image. In order to solve this problem, a novel sparse unmixing method, which considers highly similar patches in nonlocal regions of a hyperspectral image, is proposed in this article. This method exploits spectral correlation by using collaborative sparsity regularization and spatial information by employing total variation and weighted nonlocal low-rank tensor regularization. To effectively utilize the tensor decomposition, nonlocal similar patches are first grouped together. Then, these nonlocal patches are stacked to form a patch group tensor. Finally, weighted low-rank tensor regularization is enforced to constrain the patch group to obtain an estimated low-rank abundance image. Experiments on simulated and real hyperspectral datasets validated the superiority of the proposed method in better maintaining fine details and obtaining better unmixing results.