Automatic change detection of urban land-cover based on SVM classification

The reliability of support vector machines for classifying multi-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for urban land cover in Wuhan, Hubei province of China. Firstly, radiation rectification, normalization processing and geometry registration are made between the bi-temporal images. Secondly, SVM approach is used in our study to classify sorts and land use types from bi-temporal images. Thirdly, build matrix of change detection in basis of the potential types of change. Post-classification compare are proposed pixel-by-pixel. According to the sort of change of every pixel, new value is assigned on the base of change matrix. The output is image of change. Lastly, the process and pattern of the urban land use change in the Wuhan district was finally revealed from 2009 to 2013 in our study.