Object-oriented SVM classifier for ALSAT-2A high spatial resolution imagery: A case study of algiers urban area

The development of robust object-oriented classification approaches suitable for medium to high spatial resolution satellite imagery provides a valid alternative to traditional pixel-based classification approaches. In the past, Support Vector Machines (SVM) have been tested and evaluated only as pixel-based image classifiers. Moving from pixel-based analysis to object-based analysis, a fuzzy classification concept is generally used through eCognition software [1]. In this paper, we propose an object-oriented classification system based on SVM approach. By using a suitable scale during a multi-resolution segmentation step, obtained results are compared to those produced by a pixel-based SVM classifier. The classification process is performed by using a high spatial resolution imagery acquired by the Algerian satellite ALSAT-2A. From the comparison of obtained results, it is concluded that the object-based classifier is more efficient than the pixel-based classifier for the discrimination of seven major land cover classes.