Object-based　change　detection　(CD)　is　an　effective　method　of　identifying　detailed　changes　in　land　features　by　contrastively　observing　the　same　areas　of　high-resolution　remote　sensing　images　at　different　times.　Binarization　is　the　important　step　in　partitioning　changed　and　unchanged　classes　in　the　unsupervised　domain.　We　formulate　a　novel　binarization　technique　based　on　the　Weibull　mixture　model,　where　generated　similarity　measure　images　are　modeled　using　a　mixture　of　nonnormal　Weibull　distributions.　The　parameters　in　the　model　are　further　globally　estimated　by　employing　a　genetic　algorithm.　Two　data　sets　with　high-resolution　remote　sensing　images　are　used　to　evaluate　the　effectiveness　of　the　proposed　method.　Experimental　results　demonstrate　that　the　method　allows　better　and　more　robust　unsupervised　object-based　CD　than　do　state-of-the-art　threshold-based　and　clustering-based　methods.　Advantages　of　the　proposed　method　are　embodied　in　the　modeling　of　relatively　few　data　of　the　changed　class　with　a　skewed　and　long　tail　distribution.