In this paper, we propose a novel supervised approach to classification of high spatial resolution images. This approach is aimed at obtaining accurate and reliable classification maps by properly preserving the geometrical details present in the images. It is based on: i) a feature-extraction module, which exploits an adaptive, multilevel and hierarchical modeling of the investigated scene; ii) a support vector machine (SVM) classifier. The choice to adopt an SVM classification technique is motivated by the high number of parameters derived from the feature-extraction phase, which requires a classifier suitable to the analysis of hyperdimensional features spaces. Experimental results and comparisons with a standard technique developed for the analysis of high-spatial resolution images confirm the effectiveness of the proposed approach.