Remote sensing image classification using the spatial information obtained by statistical region merging

ABSTRACTConsidering the characteristics of remote sensing images, incorporating spatial information into traditional pattern recognition technology can improve remote sensing image classification performance. We utilized statistical region merging (SRM) to obtain the spatial similarity between pixels. Then we combined the spatial similarity with the traditional spectral similarity to redefine the distance between pixels, getting a hybrid distance measure. To verify the effect of the spatial information obtained by SRM on remote sensing image classification, we applied the hybrid spatial and spectral distance to the two classifiers based on distance: the optimum-path forest (OPF) and the k-nearest neighbours (k-NN). Therefore, we constructed two contextual classifiers: OPF-SRM and k-NN-SRM. The experimental results on four real land cover images demonstrated the validity of the proposed measure of spatial information since OPF-SRM and k-NN-SRM outperformed the original classifiers and other competitive con...