端元匹配的遥感影像地物自适应光谱表征

光谱信息是遥感识别地物的依据,而目前已发展的典型地类的光谱指数模型有限,波谱库中的标准地物类型及其普适性也是有限的.鉴于此,提出一种端元匹配的地物自适应光谱表征方法,通过选取贴合影像本身的端元,并综合光谱角和距离度量对影像和端元光谱进行综合匹配.通过ETM+(Enhanced Thematic Mapper)影像上对植被、水体与美国地质调查局(United States Geological Survey,USGS)波谱库及归一化植被/水体指数的对比实验,及阴影、裸地等的验证实验,证实了该方法的有效性和普适性.

Adaptive spectral representation of remote sensing objects based on endmember matching

Spectral information is essential for objects recognition in remote sensing imagery. However, objects which have particular indices are rather few, and spectra types of spectral library and their universality are limited either. Therefore, an adaptive spectral representation method of remote sensing objects based on endmember matching is proposed. Proper endmember of imagery itself is selected. Spectral angle and distance, which is between the characteristic vectors of spectra of the interested pixel and a specific endmember, are both considered to form a new way for comprehensive spectral matching. Experiments of vegetation and water were adopted in ETM+ (Enhanced Thematic Mapper) images, and were compared to those using USGS (United States Geological Survey) library and normalized difference vegetation index (NDVI) /normalized difference water index(NDWI). Moreover, validations of shadow and bareland images were also carried out to test the effectiveness and universality of the proposed method.