基于光谱归一化的变组分光谱混合分析(NMESMA)方法及其应用

混合像元问题在低、中分辨率遥感图像中尤为突出,混合像元的存在不仅会影响地物识别和图像分类精度,也是遥感科学向定量化发展的主要障碍之一。因此,遥感图像混合像元分解及其地表覆盖信息的定量提取是近年来研究的热点。针对城市土地覆盖信息的定量提取问题,利用中等分辨率遥感图像(Landsat TM),集成光谱归一化与变组分光谱混合分析(NMESMA)的方法,基于植被-非渗透表面-土壤(V\|I\|S)模型,定量提取研究区植被、土壤和非渗透表面3类土地覆盖的定量信息,并与固定组分的光谱混合分析(LSMA)分解结果进行对比分析。结果表明:基于光谱归一化的变组分光谱混合分析(NMESMA)方法获得的精度高于传统固定组分的光谱混合分析(LSMA)结果,可有效解决光谱异质性较高的城市区域的混合像元问题,为有效提取城市地表覆盖信息,研究城市生态环境变化和模拟分析,提供了有效的信息提取方法。

Method of Normalized Multiple Endmember Spectral Mixture Analysis and Its Application

For the low and medium spatial resolution remote sensing images,the issues of mixed pixels are particularly prominent,which does not only influence the accuracy of image classification,but also greatly hinder the development of quantitative remote sensing of land surface.Therefore,mixed pixels unmixing of remote sensing images and quantitative extraction of land coverage information have attracted more interest in recent years.In this paper,focused on quantitative extraction of land cover information,a method of integration of the Normalized Spectral Mixture Analysis (NSMA) and Multiple Endmember Spectral Mixture Analysis (MESMA) have been proposed to extract the quantitative information of vegetation,soil and impervious surface using the medium-resolution remote sensing image (Landsat TM),based on Vegetation-Impervious Surface-Soil (V—I—S) Model.Comparative analysis indicated the accuracy of the unmixing by using the Normalized Multiple Endmember Spectral Mixture Analysis (NMESMA) method is higher than that of the conventional fixed endmember of spectral mixture analysis.NMESMA can solve the mixed pixel problem of the high spectral heterogeneity in urban landscape effectively.Therefore,the proposed methodology can be used to extract urban land surface information effectively,and to analyze the urban ecological environment change.