用模糊ARTMAP算法对CBERS-2数据进行分类  

Classification of CBERS-2 Imagery with Fuzzy ARTMAP Classifier

作　　者：骆成凤[1] 王长耀[2] 牛铮[2] 

机构地区：[1]中国测绘科学研究院摄影测量与遥感研究所 [2]中国科学院遥感应用研究所遥感科学国家重点实验室

出　　处：《武汉大学学报（信息科学版）》2006年第4期325-328,共4页Geomatics and Information Science of Wuhan University

基　　金：中国科学院知识创新工程重大资助项目(KZCX1-SW-01-02);国家863计划资助项目(2003AA131170)

摘　　要：用模糊ARTMAP(fuzzy adaptive resonance theory map)神经网络算法对CBERS-2数据进行了分类实践。首先介绍了模糊ARTMAP神经网络的算法原理和具体训练分类过程;然后用2004年9月新疆石河子地区的影像数据进行土地利用分类试验,并将分类结果与基于统计的最大似然法(MLC)、反向传播神经网络(BP)的分类结果作比较,总分类精度比MLC和BP算法分别提高9.9%和4.6%。结果表明,模糊ART-MAP对试验区CBERS-2影像上的裸地识别能力很强,对高分辨率的CBERS-2影像可获得很好的分类结果。This paper adopts fuzzy ARTMAP classifier to do the classification CBERS-2 imagery. The fundament theory and material process about the algorithm were firstly introduced, followed with a landuse classification experiment in Shihezi Municipality on CBERS 2 high resolution imagery. Three classifiers were compared, maximum likelihood classifier （MLC）, error back propagation classifier （BP）, fuzzy ARTMAP classifier. The assessment shows that fuzzy ARTMAP classifier has a comparably better result, with overall classification accuracy higher 9.9% and 4.6% than MLC and BP. The result also shows that fuzzy ARTMAP classifier has a better discernment to identify the bare soil on CBERS-2 imagery. Finally a superiority of the fuzzy ARTMAP classifier on CBERS-2 high resolution imagery is concluded.

关 键 词：模糊ARTMAP CBERS-2数据 分类 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] P237.9[自动化与计算机技术—控制科学与工程]