29 December 2008 Wavelet-SVM classifier based on texture features for land cover classification
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Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 72850K (2008) https://doi.org/10.1117/12.816101
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China
Abstract
Texture features are recognized to be a special hint in images, which represent the spatial relations of the gray pixels. Nowadays, the applications of the texture analysis in image classification spread abroad. Combined with wavelet multi-resolution analysis or support vector machine statistical learning theory, texture analysis could improve the quality of classification increasingly. In this paper, we focus on the land cover for the Three Gorges reservoir using remote sensing data SPOT-5, a new classification method, wavelet-SVM classifier based on texture features, is employed for this study. Compare to the traditional maximum likelihood classifier and SVM classifier only use spectrum feature, this method produces more accurate classification results. According to the real environment of the Three Gorges reservoir land cover, a best texture group is selected from several texture features. Decompose the image at different levels, which is one of the main advantage of wavelet, and then compute the texture features in every sub-image, and the next step is eliminating the redundant, every texture features are centralized on the first principal components using principal component analysis. Finally, with the first principal components inputted, we can get the classification result using SVM in every decomposition scale, but what the problem we couldn't overlook is how to select the best SVM parameters. So an iterative rule based on the classification accuracy is induced, the more accuracy, the proper parameters.
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Ning Zhang, Ning Zhang, Bingfang Wu, Bingfang Wu, Jianjun Zhu, Jianjun Zhu, Yuemin Zhou, Yuemin Zhou, Liang Zhu, Liang Zhu, } "Wavelet-SVM classifier based on texture features for land cover classification", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72850K (29 December 2008); doi: 10.1117/12.816101; https://doi.org/10.1117/12.816101
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