Land　cover　change　detection　(LCCD)　based　on　bitemporal　remote　sensing　images　has　become　a　popular　topic　in　the　field　of　remote　sensing.　Despite　numerous　methods　promoted　in　recent　decades,　an　improvement　on　the　usability　and　performance　of　these　methods　has　remained　necessary.　In　this　paper,　a　novel　LCCD　approach　based　on　the　integration　of　k-means　clustering　and　adaptive　majority　voting　(k-means_AMV)　techniques　have　been　developed.　The　proposed　k-means_AMV　method　consists　of　three　major　techniques.　First,　to　utilize　the　contextual　information　in　an　adaptive　manner,　an　adaptive　region　around　a　central　pixel　is　constructed　by　detecting　the　spectral　similarity　between　the　central　pixel　and　its　eight　neighboring　pixels.　Second,　when　the　extension　for　the　adaptive　region　is　terminated,　the　k-means　clustering　method　is　applied　to　determine　the　label　of　each　pixel　within　the　adaptive　region.　Finally,　an　existing　AMV　technique　is　used　to　refine　the　label　of　the　central　pixel　of　the　adaptive　region.　When　change　magnitude　image　(CMI)　is　scanned　and　processed　in　this　manner,　the　label　of　each　pixel　in　the　CMI　can　be　refined　and　the　binary　change　detection　map　can　be　generated.　Three　image　scenes　related　to　different　land　cover　change　events　are　adapted　to　test　the　effectiveness　and　performance　of　the　proposed　k-means_AMV　approach.　The　results　show　that　the　proposed　k-means_AMV　approach　demonstrates　better　detection　accuracies　and　visual　performance　than　that　of　the　several　extensively　used　methods.