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一、问题的提出目前,遥感应用中广泛使用的图象处理方法,是以遥感数据的光谱特征为基础,以陆地卫星多光谱数据为主要信息源。由于自然现象的复杂性和随机性,以及多光谱数据在解象力、分辨率上的局限性,这种单一信息源的分析方法除能直接反映地物的波谱特征外,与地学研究定量化的需要仍有一定的差距。对某些自然地理因子(气候、土壤等)不能直接给出其光谱信息的反映,更不能从成因上加以显示,使分类精度受到影响。为了改变遥感数据的单一信息结构,丰富影象信息含量,提高遥感解译工作的科学性和精度,本文
I. PROBLEM PROBLEM Presently, image processing methods widely used in remote sensing applications are based on the spectral characteristics of remote sensing data and take the multi-spectral data of terrestrial satellites as the main information source. Due to the complexity and randomness of natural phenomena and the limitation of resolving power and resolution of multispectral data, this single information source analysis method can not only directly reflect the spectral features of ground objects but also the quantitative needs of geosciences There is still a certain gap. For some natural geographical factors (climate, soil, etc.) can not be directly given its spectral information, but also can not be displayed from the genesis, the classification accuracy is affected. In order to change the single information structure of remote sensing data, enrich the image information content and improve the scientific and accuracy of remote sensing interpretation,