GROUND TRUTH METHOD ASSESSMENT FOR SVM-BASED LANDSCAPE CLASSIFICATION

Researches on land cover classification have a complete lack of ground truth methodology description. We propose a method to track ecotones as privileged training areas for SVM-based natural vegetation classification. This guidance method combines (i) the construction of a principal component analysis (PCA) on spectral bands and gray level co-occurence matrix texture attributes calculated on very high resolution images and (ii) the use of the Sobel's edge detection algorithm on this PCA. The experiment is successfully applied with an overall accuracy of 82 %. Using SVM, a minimum number of mixed pixels is necessary but they can help significantly in locating an appropriate hyperplane. Moreover, the presented results show that the training stage could be more influential on classifier accuracy than classifiers themselves.