A SVM ensemble approach for spectral-contextual classification of optical high spatial resolution imagery

We study a novel ensemble method as a supervised tool for the accurate classification of optical high-resolution imagery. The method uses partially optimized Support Vector Machines as basis classifier and a simple random mechanism, inspired on Random Forests, to promote diversity and include spatial information into the ensemble. Experimental results on an IKONOS image are compared with those from well-known classification methods, including spectral, contextual, and ensemble based techniques. The best results have been achieved, in both the classification accuracy and visual quality of the classification map, with the use of the proposed ensemble method.