Low rank representation for bilinear abundance estimation problem
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)(2013)
摘要
In the hyperspectral abundance estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.
更多查看译文
关键词
Hyperspectral Imagery,Spectral Unmixing,Bilinear Mixture Model,Sparse and Low Rank Representation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络