Sea　ice　change　detection　is　vital　for　understanding　climate　dynamics　and　ensuring　maritime　safety.　Existing　deep　learning　methods　often　struggle　with　the　significant　impact　of　color　variations　in　satellite　imagery,　which　can　lead　to　inaccurate　detection　results.　Moreover,　the　scarcity　of　labeled　sea　ice　change　data　limits　the　ability　of　models　to　generalize　across　diverse　scenarios.　To　address　these　challenges,　we　propose　SICNet,　a　sea　ice　change　detection　model　with　enhanced　color　robustness　and　data　efficiency.　A　wavelet-guided　color-robust　fusion　(WCF)　module　is　introduced　to　reduce　low-frequency　color　discrepancies　while　preserving　high-frequency　edge　details.　In　addition,　a　novel　change-sensitive　CutMix　(CSC)　strategy　is　used　to　augment　training　samples　by　focusing　on　regions　with　moderate　changes,　effectively　increasing　data　diversity.　Experiments　conducted　on　our　constructed　sea　ice　change　dataset　demonstrate　that　SICNet　achieves　superior　performance　and　robustness　under　varying　environmental　and　lighting　conditions.　©　2004-2012　IEEE.