The　extraction　of　urban　impervious　surface　information　plays　a　key　role　in　the　studies　of　urbanization　and　its　related　environmental　issues.　Optical　and　SAR　remote　sensing　provides　complementary　information　to　improve　the　accuracy　of　impervious　mapping.　However,　the　fusing　of　information　acquired　by　different　sensors　is　challenging.　Optical　and　SAR　features　have　distinct　characteristics,　and　require　different　classification　strategy　and　classification　types.　In　this　study,　a　strategy　of　fusing　multi-spectral　optical　and　polarimetric　SAR　data　at　decision-level　is　proposed.　Features　are　extracted　from　optical　and　SAR　data,　then　staked　auto-encoder　is　applied　to　achieve　the　land　use　and　land　cover　classification　separately.　D-S　evidence　theory　is　used　to　fuse　the　classification　result　and　the　imperious　surface　map　is　derived.　The　experiment　was　conducted　in　a　highly　complex　urban　area　of　Hong　Kong　and　the　results　proves　the　soundness　of　the　method.　©　2019　IEEE.