In　optical　satellite　imagery,　the　spectra　of　debris-covered　glaciers　are　remarkably　similar　to　those　of　mountainous　terrain　and　rocks.　This　poses　challenges　in　distinguishing　glaciers　from　the　surrounding　topography　and　makes　automated　segmentation　difficult.　To　address　this　problem,　a　Dual-Encoding　Network　(DENet)　based　on　optical　satellite　images　and　Digital　Elevation　Model　(DEM)　is　proposed.　The　network　employs　a　dual-encoding　framework　that　integrates　multi-scale　feature　extraction　and　attention　mechanisms.　By　incorporating　features　from　different　data　sources　and　extracting　DEM　topographic　parameters,　it　addresses　mis-segmentation　issues　in　source　areas　caused　by　spectrally　similar　objects　in　debris-covered　glaciers.　First,　the　satellite　image　and　DEM　extract　features　using　multi-scale　separable　convolution　attention　and　multi-kernel　attention　pooling　modules.　Subsequently,　the　obtained　two　feature　maps　are　fused.　The　multi-scale　feature　extraction　module　captures　and　integrates　information　from　glacial　images　of　various　scales　to　generate　more　comprehensive　and　enriched　feature　representations.　The　attention　mechanisms　simultaneously　assign　different　weights　to　each　channel　and　spatial　position,　focusing　on　specific　regions　at　different　scales.　This　enables　the　model　to　concentrate　on　critical　information　and　reduce　the　impact　of　redundant　features.　Experimental　results　demonstrate　that　the　model　achieves　an　average　Intersection　over　Union　(IoU)　of　94.6%,　surpassing　those　of　the　U-Net　and　DeepLabv3+　networks　by　4.53　and　3.38　percentage　points,　respectively.　This　improvement　enhances　the　accuracy　of　mountain　glacier　region　segmentation　and　validates　the　competitiveness　of　the　proposed　network　compared　with　other　existing　models.　©　2024,　Sharif　University　of　Technology.　All　rights　reserved.