SVM Classification of Urban High-Resolution Imagery Using Composite Kernels and Contour Information

The classification of remote sensing images has done great forward taking into account the image’s availability with different resolutions, as well as an abundance of very efficient classification 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, however the addition of contour information to both spectral and spatial information still less explored. 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 composite kernel that gives the best results will be used to introduce contour information in the classification process. 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 a global accuracy value of 93.52%, the addition of contour information, described by the Fourier descriptors, Hough transform and Zernike moments, allows increasing the obtained global accuracy by 1.61% which is very promising. Keywords—SVM; Contour information; Composite Kernels; Haralick feature; Satellite image; Spectral and spatial information; GLCM; Fourier descriptors; Hough transform ; Zernike moments.