Weighted Collaborative Sparse and <i>L</i> <sub>1/2</sub> Low-Rank Regularizations With Superpixel Segmentation for Hyperspectral Unmixing
Le Sun, Feiyang Wu, Chengxun He, Tianming Zhan, Wei Liu, Daopan Zhang
Abstract
In this letter, using the sparse unmixing framework, a weighted collaborative sparse and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <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.