The Classification Technology Research Based On Support Vector Machine And Spot For Hyperspectral Data

During the recent two decades, hyperspectral remote sensing has been playing an important role in both military and civil applications. It's urgent to develop fast and accurate methods in order to discover the interested information from the huge data which were produced by hyperspectral sensors.Simple SVM can only handle binary classification problems; can not directly handle multi-value classification. In the real world most of the data is multi-class data, so the simple SVM need for further expansion, so that it can solve the multi-value classification. This paper introduces several SVMs for multi-value classification, including "One against Rest", "One against One", Decision Tree and Directed Acyclic Graph SVMs, and compares their respective advantages and disadvantages. By analyzing the deficiencies of various SVMs, a new SVM method, namely, the theory of combining spot and SVM, is put forward. Finally, comparing the traditional SVM to SVM binding spot feature, the tests show the SVM combining of spot applied to hyperspectral image classification has achieved good results.The principle of SVM based on spot is to choose an appropriate scale to split the image into a series of segmentation, according to certain strategy using spectral information. And this principle ensures the spectral features of the majority of patch pixel similar. This method gathers statistics of each pixel value in the spot and obtains the mean value of each band to replace the original value of all pixels in the spot. The purpose of this classification is that the pixel having the noise brought by various causes is assimilated by the surrounding pixels to merge into a single spot. In other words, under the information of its surrounding pixels recovering the value of the pixel having noise is to not appear the fault isolation in the classification map and to avoid the salt and pepper phenomenon. The results show that this method is feasible and the classification accuracy and speed is better than traditional support vector machine.