Neural net based remote sensing image classification has obtained good results. But neural net has inherent flaws such as overfitting and local minimums. Support vector machine (SVM), which is based on Structural Risk Min- imization(SRM), has shown much better performance than most other existing machine learning methods. Using mul- ti-class SVM classifier high class rate of 95.4 is obtained. But for the class number of remote sensing image is much great, manually obtaining of training samples is a much time-consuming work. So a multi-class SVM based semi-super- vised approach is presented. It is choosed that the initial clustering centroids manually first, then label the samples as the training ones automatically with fuzzy clustering algorithm. It is believed that this method will upgrade the classifi- cation efficiency greatly with practicable class rate

Multi-class SVM Image Classification.caj

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