Weighted Collaborative Sparse and L1/2 Low-Rank Regularizations With Superpixel Segmentation for Hyperspectral Unmixing

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Sun, Le, Wu, Feiyang, He, Chengxun, Zhan, Tianming, Liu, Wei, Zhang, Daopan
• Format: Journal Article
• Language: English
• Published: IEEE 2022
• Subjects: <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">L ₁̷₂ low-rank regularization; Collaboration; Correlation; Image segmentation; Libraries; Shape; Sparse matrices; Sparse unmixing; superpixel; weighted collaborative sparse
• ISSN: 1545-598X
• DOI: 10.1109/LGRS.2020.3019427

In this letter, using the sparse unmixing framework, a weighted collaborative sparse and <inline-formula> <tex-math notation="LaTeX">L_{1/2} </tex-math></inline-formula> low-rank regularization with superpixel segmentation method is proposed for hyperspectral unmixing. The method outlined here first uses superpixel segmentation to obtain local homogeneous regions. The reason for this approach is that the shape and size of superpixels are adaptive, which are better for obtaining homogeneous regions than square patches. Next, the weighted collaborative sparse term and <inline-formula> <tex-math notation="LaTeX">L_{1/2} </tex-math></inline-formula> low-rank regularization were utilized to exploit the spatial and spectral correlation of each superpixel. In addition, the smoothness between adjacent pixels is enforced by total variation regularization. Finally, the proposed method and several state-of-the-art methods were tested on two simulated data sets and two real data sets. The results demonstrate the superiority of the method proposed here.