The Classification Method of Multi-spectral Remote Sensing Images Based on Self-adaptive Minimum Distance Adjustment

Abstract : The phenomenon of “Same Object with Different Spectra” in the issue of multi-spectral remote sensing images land use classification makes major effects on improving accuracy. The paper based on the analysis of modeling on classification problems, proposed a method based on minimum distance self-adaptive adjustment to realize the split of cluster centers and solved the problem of identified scope intersection leading to improving the accuracy in the classifying methods difficultly. By experiments compared with the traditional methods, it can improve classification accuracy about 4% and the results prove the validity of this method.
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Communication dans un congrès
Daoliang Li; Yingyi Chen. 6th Computer and Computing Technologies in Agriculture (CCTA), Oct 2012, Zhangjiajie, China. Springer, IFIP Advances in Information and Communication Technology, AICT-393 (Part II), pp.430-437, 2013, Computer and Computing Technologies in Agriculture VI. 〈10.1007/978-3-642-36137-1_50〉
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Junhua Liu, Chengming Zhang, Shujing Wan. The Classification Method of Multi-spectral Remote Sensing Images Based on Self-adaptive Minimum Distance Adjustment. Daoliang Li; Yingyi Chen. 6th Computer and Computing Technologies in Agriculture (CCTA), Oct 2012, Zhangjiajie, China. Springer, IFIP Advances in Information and Communication Technology, AICT-393 (Part II), pp.430-437, 2013, Computer and Computing Technologies in Agriculture VI. 〈10.1007/978-3-642-36137-1_50〉. 〈hal-01348260〉

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