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　CDRCW　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　CDRVW　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).　(C)　2020　Society　for　Imaging　Science　and　Technology.