小波包分解支持下的高光谱混合像元盲分解  

Blind Unmixing of Hyperspectral Mixed Pixels Assisted by Wavelet Packet Decomposition

作　　者：李熙[1] 陈学泓[2] 陈晓玲[1] 田礼乔[1] 陈锋锐[3] 

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,武汉430079 [2]名古屋大学环境学研究科 [3]中国科学院遥感应用研究所,北京100101

出　　处：《光子学报》2011年第6期835-842,共8页Acta Photonica Sinica

基　　金：武汉大学自主科研项目(No.904275839);武汉市科技计划项目(No.200970634267);国家自然基金委创新研究群体科学基金项目(No.41021061);国家重点基础研究发展计划项目(No.2009CB723905)资助

摘　　要：提出将小波包辅助下子带分解的独立成分分析用于高光谱混合像元盲分解.利用小波包分解改进独立成分分析技术,并考虑到高光谱数据的特点提出了高光谱混合像元盲分解方法,该方法能克服独立成分分析方法要求端元光谱统计独立带来的问题.利用两组合成数据和三组室内实测数据对本算法进行测试,证明了本算法能较为准确的提取端元光谱波形和端元丰度,其准确度明显优于独立成分分析方法.该方法为高光谱遥感影像的盲分解提供了一条新的途径.A method called ＂wavelet packet approach to sub-band decomposition independent component analysis＂ was introduced to unmix hyperspectral mixed pixels.Using wavelet packet decomposition technique to improve independent component analysis,a blind unmixing method for hyperspectral data was proposed considering the characteristics of hyperspectral data,and this method could overcome the drawback derived from statistical independence assumption in independent component analysis.Two groups of synthetic data and three groups of indoor data were used to evaluate this method.The experiments show that spectra and fractional abundances of the endmemebers can be retrieved precisely using the proposed method,and its accuracy is significantly higher than that of independent component analysis.This approach is useful for blind unmixing of hyperspectral remotely sensed imagery.

关 键 词：遥感 小波包分解 混合像元 独立成分分析 线性光谱混合模型 

分 类 号：P751.1[交通运输工程—港口、海岸及近海工程]