Heterogeneous　change　detection　is　a　task　of　considerable　practical　importance　and　significant　challenge　in　remote　sensing.　Heterogeneous　change　detection　involves　identifying　change　areas　using　remote　sensing　images　obtained　from　different　sensors　or　imaging　conditions.　Recently,　research　has　focused　on　feature　space　translation　methods　based　on　deep　learning　technology　for　heterogeneous　images.　However,　these　types　of　methods　often　lead　to　the　loss　of　original　image　information,　and　the　translated　features　cannot　be　efficiently　compared,　further　limiting　the　accuracy　of　change　detection.　For　these　issues,　we　propose　a　cross-modal　feature　interaction　network　(CMFINet).　Specifically,　CMFINet　introduces　a　cross-modal　interaction　module　(CMIM),　which　facilitates　the　interaction　between　heterogeneous　features　through　attention　exchange.　This　approach　promotes　consistent　representation　of　heterogeneous　features　while　preserving　image　characteristics.　Additionally,　we　design　a　differential　feature　extraction　module　(DFEM)　to　enhance　the　extraction　of　true　change　features　from　spatial　and　channel　dimensions,　facilitating　efficient　comparison　after　feature　interaction.　Extensive　experiments　conducted　on　the　California,　Toulouse,　and　Wuhan　datasets　demonstrate　that　CMFINet　outperforms　eight　existing　methods　in　identifying　change　areas　in　different　scenes　from　multimodal　images.　Compared　to　the　existing　methods　applied　to　the　three　datasets,　CMFINet　achieved　the　highest　F1　scores　of　83.93%,　75.65%,　and　95.42%,　and　the　highest　mIoU　values　of　85.38%,　78.34%,　and　94.87%,　respectively.　The　results　demonstrate　the　effectiveness　and　applicability　of　CMFINet　in　heterogeneous　change　detection.