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

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 nove...