The Remote Sensing Image Classification Based On Support Vector Machine And Fuzzy Post-Process

Remote sensing image classification is one of the approaches to obtain information from images. Nowadays, the traditional visual interpretation cannot meet large data sets processing anymore, so the research on computer classification is significant for batch processing and improving efficiency.Support Vector Machine (SVM) becomes a hot issue of computer classification field in recent years. It was proposed in the middle of 1990s and has good generalization ability due to its theoretical background on statistic learning theory (SLT), quadratic programming (QP) and kernel method. Especially when solving the classification problems in small sample set, this algorithm shows high recognition rate and statistical stability. As we know, the training samples which is usually finite in remote sensing image classification, can be considered as small sample sets, therefore, the study on remote sensing image classification based on SVM has practical value and vast development space.The new approach of remote sensing image classification based on support vector machine and fuzzy post-process is applied in this paper. Based on some related theories, the two-class and the multi-class classification are analyzed, particularly on the advantages and disadvantages of 1-v-r SVMs and 1-v-1 SVMs. Subsequently, a fuzzy membership function is introduced to solve the problems. At last, on the TM5, 4, 3 bands-synthetic images of Wuhan Area, a 1-v-1 SVMs model with a fuzzy post-process method is constructed. Therefore, the classification images and data tables are produced.The maximum likelihood classification experiment is also implemented in order to comparing with SVM and SVM combined fuzzy algorithms. Qualitative images and quantitative data show that, the remote sensing image classification based on SVM and fuzzy post-process method is feasible and has best recognition rate.