高光谱端元自动确定与提取的迭代算法

Automatic identification and extraction of endmember from hyperspectral imagery by iterative unmixing

针对端元提取算法依赖人工确定端元数量的问题,提出一种端元自动确定与提取的迭代算法。首先,通过统计分析获得像元相似性阈值,确定候选端元判据;其次,对候选端元进行内、外部相关性判断,对端元光谱集进行病态矩阵规避判断;最后,以候选端元判据为迭代终止条件,当图像空间不存在候选端元时,获得端元集合并确定端元数。实验结果表明,该方法正确有效,可以避免顺序端元提取方法的错误风险,提高端元提取自动化程度。

Current algorithms of endmember extraction basically need manually determining the number of endmembers, which is not conducive to automatically process. The paper puts forward iterative algorithm for automatic identification and extraction of endmember. First, we obtain the similarity threshold among pixels by statistical analysis, and determine the criterion of candidate endmembers. Then, the internal and external correlation judgments of candidate endmembers are done, and ill-conditioned matrix to circumvent judgment on endmember spectral set is conducted. Finally, the criterion of candidate endmembers is the end of the iterative conditions. When the hyperspectral image contains no candidate endmembers, the endmember spectral set is got and the numbers of endmembers are determined. Experiments show the effectiveness of this method, by which the error risk of sequential endmember extraction algorithm can be avoided, and the degree of automation is improved.