Change　detection　is　an　increasingly　important　research　topic　in　remote　sensing　application.　Previous　studies　achieved　land　cover　change　detection　(LCCD)　using　bi-temporal　remote　sensing　images.　However,　many　widely　used　methods　detected　change　depending　on　a　series　of　parameters,　and　determining　parameters　is　time-consuming.　Furthermore,　numerous　methods　are　data-dependent.　Therefore,　their　degree　of　automation　should　be　improved　significantly.　Three　techniques,　which　consist　of　a　semi-automatic　change　detection　system,　are　proposed　for　LCCD　to　overcome　the　abovementioned　drawbacks.　The　three　techniques　are　as　follows:　(1)　change　magnitude　image　(CMI)　noise　reduction　is　based　on　Gaussian　filter　(GF),　which　is　coupled　with　OTSU　for　reducing　CMI　noise　automatically　using　an　iterative　optimization　strategy;　(2)　a　method　based　on　histogram　curve　fitting　is　suggested　to　predict　the　threshold　range　for　parameter　determination;　and　(3)　a　modified　region　growing　algorithm　is　built　for　iteratively　constructing　the　final　change　detection　map.　The　detection　accuracies　of　the　proposed　system　are　investigated　through　four　experiments　with　different　bi-temporal　image　scenes.　Compared　with　several　widely　used　change　detection　methods,　the　proposed　system　can　be　applied　to　detect　land　cover　change　with　high　accuracy　and　flexibility.　This　work　is　an　attempt　to　provide　a　change　detection　system　that　is　compatible　with　remote　sensing　images　with　high　and　median-low　spatial　resolution.　©　2017　by　the　authors.