高光谱成像
正规化(语言学)
计算机科学
人工智能
模式识别(心理学)
先验概率
回归
事先信息
数学
贝叶斯概率
统计
作者
Xiangfei Shen,Haijun Liu,Xinzheng Zhang,Kai Qin,Xichuan Zhou
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:19: 1-5
标识
DOI:10.1109/lgrs.2022.3218730
摘要
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, called 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.
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