基于分层多端元混合像元分解的水稻面积信息提取

摘要

为了解决中低分辨率遥感影像混合像元问题以提高水稻种植信息的提取精度,该文提出了一种基于层次分类与多端元混合像元分解相结合提取水稻面积信息的方法(stratified multiple endmember spectral mixture analysis,SMESMA).层次分类有效降低了地物复杂度,而多端元混合像元分解通过对每一类地物选取多个端元光谱参与解混,克服了“同物异谱”造成的光谱变异问题,两者结合可有效提高分类精度.以江苏如皋市为研究区,基于HJ-1B CCD影像,分3个层次,当某类地物信息被提取后便将其从影像中去除,进行下一层次分类,各层次均采用多端元混合像元分解方法,综合EARMSE、MASA、CoB等算法以选取最佳端元,实现了如皋市水稻种植面积信息有效提取.结果显示SMESMA法分类精度达85.78％,kappa系数为0.85,基于最大似然分类法(MLC)的分类精度为79.1％,kappa系数为0.78.表明SMESMA是一种适合基于中低分辨率影像进行作物分类和面积提取的有效方法.%To resolve the serious pixel un-mixing problem produced by coarse spatial resolutions sensors, and improve the extraction accuracy of plant area for paddy rice, the stratified multiple endmember spectral mixture analysis (SMESMA) method was proposed in this paper. The complexity of landscape will be mitigated using stratified classification method, and the number and types of endmembers are allowed to vary in a per-pixel basis by multiple endmember spectral mixture method, which can overcome the spectral variations within classes. The accuracy of classification was improved significantly by combining these two methods. In this study, the HJ-1B CCD image was stratified into three stratifications. A landscape will be removed from the image after extracted, and the next classification will run based on the new stratified image. Multiple endmember spectral mixture analysis was applied to map the stratification images, and the optimized endmembers was determined by EAR、MASA and CoB methods. The results showed that that SMESMA had better classification accuracy of 85.78% and kappa coefficient of 0.85 than that of 79.1% and 0.78 by per-pixel based maximum likelihood classifier (MLC), which indicated that SMESMA was a useful classifier and method for paddy cultivation area extracting with coarse spatial resolution image.