4.7 Article

Superpixel-Guided Local Sparsity Prior for Hyperspectral Sparse Regression Unmixing

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2022.3218730

关键词

Global sparsity; hyperspectral unmixing; local sparsity; sparse regression; sparse unmixing

资金

  1. National Natural Science Foundation of China [61971072, 62001063]
  2. Graduate Research and Innovation Foundation of Chongqing, China [CYB22068]
  3. China Postdoctoral Science Foundation [2020M673135]

向作者/读者索取更多资源

The study introduces a local-global sparse regression unmixing method, which combines local sparsity regularization and global sparsity regularization to effectively estimate the abundance of a given image. Experimental results demonstrate the effectiveness of the proposed algorithm.
Sparse regression relaxes the difficulties of blind unmixing of hyperspectral data thanks to the spectral library. Many investigations, however, attach importance to global priors such as sparsity and low rankness. This letter proposes a local-global-based sparse regression unmixing method (LGSU), by introducing a local sparsity regularization to help boost the unmixing performance that only considers global sparsity. The proposed LGSU first uses a superpixel-based technique to yield a set of homogeneous superpixels for guiding local sparse regularization purposes. LGSU then considers a traditional l(1) regularization to enhance global sparsity. Coupling with local and global sparsity constraints, the proposed LGSU can effectively estimate the abundance of a given image via the alternating direction method of multipliers. Experimental results obtained from synthetic and real hyperspectral images demonstrate the effectiveness of the proposed algorithm.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据