由粗到细的高光谱图像多端元光谱混合分析

光谱可变性是影响高光谱图像光谱混合分析精度的重要因素，多端元光谱混合分析是解决该问题的有效手段。为了降低光谱混合分析时间复杂度的同时提高其精度，提出了一种由粗到细的多端元光谱混合分析算法，该算法首先基于扩展的端元集对每个像元进行全约束光谱混合粗分析，确定含所有地物的初始端元集，在此基础上进一步进行精细光谱混合分析，迭代光谱混合分析构建端元子集，最终根据重构误差变化量确定各个像元的最优端元集。实验结果表明:相比迭代光谱混合分析法和分层多端元光谱混合分析法，所提出的由粗到细的高光谱图像多端元光谱混合分析能有效降低算法反演丰度误差并改善计算效率。

A Corse-to-Fine Scheme for Multiple Endmember Spectral Mixture Analysis of Hyperspectral Images

Spectral variability is an important factor which influences the accuracy of spectral analysis in hyperspectral images. Multiple endmembers spectral mixture analysis is an effective method to solve this problem. In order to reduce the time complexity of spectral mixing analysis and improve the accuracy in the same time, a multiple endmember spectral mixture analysis algorithm based on corse-to-fine scheme is proposed. Based on the extended endmember set for each pixel, the proposed algorithm firstly make fully-constrained spectral mixing coarse analysis to determine the initial set of end-members containing all land cover material. On this basis, the algorithm further conducts fine spectral mixture analysis, iterative spectral mixture analysis to build end-member subsets and the optimal end-member set is finally determined according to the variation of reconstruction error. The experimental results show that compared with the iterative spectral mixture analysis method and the hierarchical multi-endmember spectral mixture analysis algorithm, the proposed algorithm reduces the error of inversion abundance and improves computational efficiency greatly.