In　the　field　of　heterogeneous　remote　sensing　image　change　detection,　particularly　in　natural　disaster　assessment,　the　fusion　of　optical　and　synthetic　aperture　radar　(SAR)　images　is　widely　applied.　However,　due　to　differences　in　resolution　and　imaging　mechanisms　of　heterogeneous　remote　sensing　data,　existing　deep　learning　methods　often　lose　original　image　features　and　introduce　additional　noise,　leading　to　suboptimal　detection　results.　To　resolve　these　challenges,　this　study　introduces　a　Mamba-based　Siamese　Network　(Mamba-OSCDNet)　that　combines　cross-attention　fusion　with　contrastive　learning.　The　network　introduces　a　Cross-Attention-based　Mamba　Fusion　Mechanism　(CAMFM),　which　effectively　enhances　the　feature　interaction　and　channel　information　between　heterogeneous　images.　In　addition,　to　alleviate　the　category　imbalance　problem,　this　paper　adopts　a　contrastive　learning　strategy　and　designs　a　weighted　composite　loss　function,　which　significantly　reduces　the　leakage　detection　rate.　The　findings　show　that　the　new　method　outperforms　current　techniques　on　various　benchmark　datasets.　Taking　the　Gloucester　I　dataset　as　an　example,　the　method　attains　an　F1　score　of　95.11%　and　an　overall　accuracy　of　99.37%,　representing　improvements　of　7.75%　and　1.08%,　respectively,　over　the　best-performing　baseline.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.