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中文摘要：
针对人工样本选择和端元提取存在的不确定性和工作量大等缺点,提出一种集成非监督分类、纯净像元指数计算、线性光谱混合模型和凸面单形体理论的自动端元提取算法,能够有效地提取端元用于高光谱遥感影像分类和混合像元分解。利用北京昌平地区的OMIS高光谱遥感数据进行了验证,结果表明算法可行有效,自动化程度较高,作为训练样本进行分类能够获得较高精度,优于常规方法。
英文摘要：
An automatic endmember extraction algorithm is proposed based on unsupervised classification,pixel purity index,liner spectral mixing model and simplex of convex geometry concepts.This proposed algorithm can avoid the effects of uncertainty,heavy workload and other shortcomings of existing artificial sampling procedures.The approach is experimented by an example of OMIS hyperspectral image,and the experimental result indicates that the algorithm is effective and has high degree of automation.
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