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| Application of RunLength Texture Features to SPOT Remote Sensing Image Classification |
| CAO ZhiGuo, XIAO Yang, ZOU LaMei |
| Institute of Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074 |
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Abstract Combined with neural network, a method for remote sensing image classification based on runlength features is proposed. According to the criterion of variances between and intra classes, the efficient features are selected and the redundant ones are excluded successfully by the method of rough set. Runlength features, cooccurrence features, gray levelgradient cooccurrence features and gray levelsmoothed cooccurrence features are respectively used as inputs of three types of classifiers: BP net, RBF net and a nearest neighbor classifier-KNN method, when applying remote sensing classification for large scale panchromatic SPOT images with high spatial resolution. The result demonstrates the efficiency of the proposed algorithm.
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Received: 20 September 2006
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2008, Vol. 21 
