The Classification Research of Support Vector Machine Based on Spot for Hyperspectral Remote Sensing Application

The principle of Support Vector Machine 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 is olation 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.