Wavelet-SVM classifier based on texture features for land cover classification

Ning Zhang; Bingfang Wu; Jianjun Zhu; Yuemin Zhou; Liang Zhu

doi:10.1117/12.816101

29 December 2008 Wavelet-SVM classifier based on texture features for land cover classification

Ning Zhang, Bingfang Wu, Jianjun Zhu, Yuemin Zhou, Liang Zhu

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.

Citation Download Citation

Ning Zhang, Bingfang Wu, Jianjun Zhu, Yuemin Zhou, and 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); https://doi.org/10.1117/12.816101

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available