Abstract—The classification of remotely sensed images knows a large progress taking in consideration the availability of images with different resolutions as well as the abundance of classification’s algorithms. A number of works have shown promising results by the fusion of spatial and spectral information using Support vector machines (SVM) which are a group of supervised classification algorithms that have been recently used in the remote sensing field. For this purpose we propose a methodology exploiting the properties of Mercer’s kernels to construct a family of composite kernels that easily combine multi-spectral features and Haralick texture features as data source. The proposed approach was tested on common scenes of urban imagery. The three different kernels tested allow a significant improvement of the classification performances and a flexibility to balance between the spatial and spectral information in the classifier. The experimental results indicate an accuracy value of 92.55% which is very promising.

Index Terms—SVM, classification, composite kernels, haralick features, satellite image, spectral and spatial information

Cite: Aissam Bekkari, Soufiane Idbraim, Driss Mammass, Mostafa El Yassa, and Danielle Ducr, "SVM Classification of High Resolution Urban Satellites Images using Composite Kernels and Haralick Features," Journal of Emerging Technologies in Web Intelligence, Vol. 6, No. 1, pp. 69-74, February 2014. doi:10.4304/jetwi.6.1.69-74