A classification problem involving multi-class samples is typically divided into a set of two-class sub-problems. The pairwise probabilities produced by the binary classifiers are subsequently combined to generate a final result. However, only the binary classifiers that have been trained with the unknown real class of an unlabeled sample are relevant to the multi-class problem. A distance-based relative competence weighting (DRCW) combination mechanism can estimate the competence of the binary classifiers. In this work, we adapt the DRCW mechanism to the support vector machine (SVM) approach for the classification of remote sensing images. The application of DRCW can allow the competence of a binary classifier to be estimated from the spectral information. It is therefore possible to distinguish the relevant and irrelevant binary classifiers. The SVM+DRCW classification approach is applied to analyzing the land-use/land-cover patterns in Guangzhou, China from the remotely sensed images from Landsat-5 TM and SPOT-5. The results show that the SVM+DRVW approach can achieve higher classification accuracies compared to the conventional SVM and SVMs combined with other combination mechanisms such as weighted voting (WV) and probability estimates by pairwise coupling (PE).