高光谱图像的空间分辨率较低, 导致大量混合像元存在于高光谱图像中。混合像元的存在是使高光谱图像目标分类准确率降低的主要原因之一。高光谱像元解混在高光谱遥感图像处理中具有非常重要的意义。高光谱像元解混主要分为线性和非线性光谱解混两种方法,研究最广泛的是线性光谱解混。归纳了线性光谱解混的两个步骤: (1)提取纯净像元中地物的光谱信号, 即提取端元,这是关键步骤; (2)利用端元的加权线性组合对混合像元进行光谱解混, 即丰度反演。简述了端元提取及丰度反演研究的主要进展,介绍了端元提取的几种典型算法。通过归纳、对比和分析, 总结了不同端元提取方法的特点, 并对高光谱解混的研究前景进行了展望。

Because spatial resolution of hyper-spectral images is low, a large number of the mixed pixels were in hyper-spectral images. The presence of the mixed pixels is one of the main reasons of the low accuracy of target classification in hyper-spectral images. Hyper spectral pixel unmixing is of great importance in hyper-spectral remote sensing image processing. Hyper-spectral unmixing is divided into two methods: linear and nonlinear spectral unmixing. Linear spectral unmixing has been studied most widely. Two steps of linear spectral mixing are summed up: firstly, spectral signals of ground objects in the pure pixels are extracted, that is, end-members are extracted. It is the key step. The weighted linear combination of end-members is used to unmix the spectral image of the mixed pixels, that is, the abundance inversion. Main progress of end-member extraction and abundances inversion is briefly introduced, and several typical algorithms for end-member extraction are introduced. Through summing-up, contrasting and analyzing, the characteristics of different endmember extraction methods are summarized. The prospect of hyperspectral unmixing is prospected.