SVM classification of high resolution urban satellites Images using Haralick features

The classification of remotely sensed images knows a large progress taking into consideration the availability of images with different resolutions as well as the abundance of classification’s algorithms. SVMs (Support Vector Machines) are a group of supervised classification algorithms that have been recently used in the remote sensing field, a number of works have shown promising results by the fusion of spatial and spectral information using SVM. For this purpose, we propose a methodology allowing to combine these two information. The SVM classification was conducted using a combination of multi-spectral features and Haralick texture features as data source. We have used homogeneity, contrast, correlation, entropy and local homogeneity, which were the best texture features to improve the classification algorithm. The result will be compared with both a standard SVM classifier and a SVM classifier with a Graph Cuts approach that introduces spatial domain information applied as a post-classification. The proposed approach was tested on common scenes of urban imagery. Results showed that SVMs, especially with the use of Haralick texture features, outperform the SVM classifier with post-processing in term of the global accuracy. The experimental results indicate a mean accuracy value of 94.045 % which is very promising.