压缩感知光谱重构中的字典原子选取优化方法

Improved Method of Dictionary Atom Selection in Compressive Sensing Spectral Reconstruction

针对常用的迭代追踪类算法难以保证低采样下光谱重构的成功率与精度的问题,提出了一种在低采样下光谱重构中字典原子选取的优化方法。利用AVIRIS和ROSIS高光谱数据构建光谱稀疏字典并进行压缩感知光谱重构实验,分别从光谱重构精度、稀疏成分提取能力、光谱重构的成功率和光谱识别的准确率等不同角度进行了分析。实验结果表明,本文方法不仅优于传统的匹配追踪算法,同时也优于公认的精度较高的FOCUSS、MSBL等其他类型的算法。

To solve the problem that common iterative pursuit algorltnm has low success rate and precision of spectrum reconstruction in low sampling rate, an improve atom selection method of the spectrum reconstruction in low sampling rate is proposed. The spectrum dictionaries are constructed with AVIRIS and ROSIS hyperspectral data and the compressive sensing hyperspectral reconstruction experiment is conducted. The spectrum reconstruction precision, the sparse component extraction ability, and the success rate and accuracy rate in spectral reconstruction are analyzed in different views, respectively. Experimental results show that the proposed method is much better than conventional matching pursuit algorithms and also superior to the well know high precision method such as FOCUSS and MSBL algorithms.