基于流形学习的混合光谱解混分析  

Analysis on spectral unmixing based on manifold learning

作　　者：丁玲[1,2] 唐娉[1] 李宏益[1] 

机构地区：[1]中国科学院遥感与数字地球研究所,北京100101 [2]中国科学院大学,北京100049

出　　处：《红外与激光工程》2013年第9期2421-2425,共5页Infrared and Laser Engineering

基　　金：国家863计划(2009AA122002)

摘　　要：光谱解混分析的重要研究内容是计算分析各地物类别成分在混合像素内所占的比例技术。文中以实测高光谱数据为研究对象,针对高光谱数据具有高维度数、严重的光谱混合等特点,基于流形学习中局部线性嵌入(LLE)算法的思想,提出了一种约束最小乘方局部线性加权回归(CLS-LLWR)建模方法。通过4种典型地物的光谱吸收特征差异分析,从它们不同比例组合下的实测混合光谱中选取了不同波段范围,分别对该模型预测覆盖度信息能力进行了验证分析。最后,将CLS-LLWR模型与主成分回归(PCR)和偏最小二乘回归(PLSR)模型,通过计算预测标准误差(SE)进行了对比分析。结果表明,CLS-LLWR模型有较好的预测能力。这为流形学习在高光谱遥感图像信息提取方面进行了有意的探索。The main study on spectral unmixing is to develop a regression between mixed spectral features of main land-cover types and their responding fractional cover. Studying on in situ spectral reflectance data, based on one of the best known algorithms of manifold learning, locally linear embedding （LLE）, a new modeling method named constrained least squares locally linear weighted regression （CLS-LLWR） was proposed. Spectral reflectance of four kinds of the mixed land-cover types in different percentages was measured and preliminarily analyzed. The model CLS-LLWR was verified by predicting fractional cover of main land- cover types. Compared with principal component regression （PCR） and partial least squares regression （PLSR）, through comparison and analysis of the standard error of prediction（SE）, the result shows that the CLS-LLWR has better predictability. This study indicates that manifold study has the potential for the information extraction of mixed land cover types in hyperspectral image.

关 键 词：光谱解混 流形学习 局部线性加权回归 覆盖度 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]