端元提取是混合像元分解的关键, 研究其算法在高精度的地物识别、 丰度反演和定量遥感等方面具有重要意义。通过研究高光谱遥感影像光谱特征, 结合信息熵理论, 应用高斯分布函数, 建立了一种新的高光谱影像端元提取算法, 即光谱最小信息熵(spectral minimum shannon entropy, SMSE)算法。将该算法应用于AVRIRS高光谱影像的端元光谱提取, 并经过与美国地质勘探局(United States Geological Survey, USGS)波谱库中的数据匹配, 得知其提取端元的精度较高。同时, 通过与经典的纯净像元指数(pixel purity index, PPI)和连续最大角凸锥(sequential maximum angle convex cone, SMACC)等端元提取算法进行实验比较和结果综合分析, 发现光谱最小信息熵算法提取端元光谱效率更高、 精度更好。此外, 分别利用SMACC和SMSE提取Hyperion高光谱影像端元, 得出SMSE的端元提取效果好于SMACC, 从而可认为SMSE算法具有一定普适性。

It’s significant to study the algorithm of endmember extraction, which is the key for pixel unmixing, in the fields of feature identification, abundance inversion, quantitative remote sensing and so on. Based on the theory of shannon entropy and Gaussian distribution function, a new algorithm, named spectral minimum shannon entropy (SMSE) method for extracting endmembers of hyperspectral images, is proposed in the present paper after analyzing the characteristics of spectra of the hyperspectral images. This algorithm was applied to extract the endmembers of an AVRIRS hyperspectral image, it was found that these extracted endmember spectra have higher precision by matching with the spectral library of United States Geological Survey(USGS). At the same time, it was also found that the SMSE algorithm has better efficiency and accuracy for extracting endmember spectra through comparing and analyzing comprehensively the results of endmember extraction of the experimental data by using the methods of SMSE, pixel purity index(PPI), sequential maximum angle convex cone(SMACC) and so on. In addition, the SMACC and SMSE are used to extract the endmembers in a Hyperion hyperspectral image, and it is concluded that the results of the SMSE is better than the SMACC’s. Thus, the SMSE algorithm can be thought to have a certain degree of universal applicability.