1. 中国科学院遥感应用研究所，北京 100101 2. Department of Geography，UCLA，CA90095 1524，USA 3. 中国科学院研究生院，北京 100049
A Spatial Adaptive Algorithm for Endmember Extraction on Multispectral Remote Sensing Image
ZHU Chang-ming1, 3*, LUO Jian-cheng1, SHEN Zhan-feng1, LI Jun-li2, HU Xiao-dong1
1. Institute of Remote Sensing Applications, Chinese Academy of Sciences，Beijing 100101，China 2. Department of Geography，UCLA，CA90095 1524, USA 3. Graduate University of Chinese Academy of Sciences，Beijing 100049，China
Abstract：Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks’ landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What’s more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.
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