Multi-objective based spectral unmixing for hyperspectral images

详细信息    查看全文

• 作者：Xia Xu^a ; Zhenwei Shi^a ; ^b ; ^c ; ^{shizhenwei@buaa.edu.cn}
• 关键词：Hyperspectral image ; Sparse unmixing ; Multi-objective optimization ; l0 problem ; Binary coding
• 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：124
• 期：Complete
• 页码：54-69
• 全文大小：8163 K
• 卷排序：124

文摘

Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l0l0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l0l0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l0l0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.