This　paper　developed　an　approach　to　determine　optimal　parameters,　C　and　s,　for　support　vector　domain　description　(SVDD)　model　to　map　specific　land　cover　from　integrating　of　training　and　window-based　validation　sets　(WVS-SVDD).　The　validation　set　based　on　window-based　approach　made　a　tighten　hypersphere　because　of　compact　constraint　by　the　outlier　pixels　which　were　located　closely　to　the　target　class　in　the　feature　space.　The　target　land　coves　of　wheat　and　bare　land　were　considered　to　test　the　proposed　method＇s　performance.　The　overall　accuracy　for　wheat　reached　as　high　as　94.37%.　However,　the　underestimation　of　wheat,　only　71.12%　of　the　user＇s　accuracy,　attributed　to　the　validation　set　covering　a　small　portion　of　wheat　spectra.　The　larger　window　sizes　were　tested　to　achieve　more　wheat　pixels＇　samples　for　validation　set.　The　results　showed　that　wheat　accuracy　were　improved　along　with　window　size　increasing　and　the　overall　accuracies　were　higher　than　88%　The　bare　land　as　more　heterogeneous　land　cover　against　wheat　was　selected　to　analyze　the　applicability　and　suitability　of　WVS-SVDD,　SVDD　classification　was　conducted　and　compared　to　the　support　vector　machine　(SVM)　method　as　benchmark.　The　producer＇s　and　user＇s　accuracies　for　bare　land　were　over　80%　at　2.4-m　resolution　scale　and　the　overall　accuracies　were　similar　to　those　produced　by　the　SVM　at　coarser　spatial　resolutions,　highlighting　the　applicability　of　WVS-SVDD.　Therefore,　the　developed　method　showed　its　advantages　using　the　optimal　parameters,　C　and　s,　for　mapping　homogeneous　wheat　and　heterogeneous　bare　land,　which　exhibits　great　potentials　to　achieve　highly　accurate　specific　land　cover.　©　2017　IEEE.