Iterative Support Vector Machine for Hyperspectral Image Classification

Abstract

In hyperspectral image classification spectral information and spatial information are always integrated to improve the classification accuracy. This paper develops an iterative version of support vector machine, to be called iterative SVM (ISVM) to perform hyperspectral image classification by extracting spatial information iteratively via feedback loops. In processing ISVM an initial hyperspectral data cube is obtained by combining the original image and its first principal component. SVM is then implemented to the resulting data cube to produce an initial classification map. In each feedback loop, a Gaussian filter is applied to obtain the spatial information of the SVM-classification map so that the Gaussian-filtered map is further fed back to combine with the currently processed hyperspectral cube for the next round of iteration. As for terminating the iterative process an automatic stopping rule is also developed. To evaluate the performance of ISVM real image experiments are conducted in comparison with state-of-the-art spectral-spatial hyperspectral classification methods. The experiment results demonstrate that ISVM performed better by providing higher classification accuracy.