Multi-source complementary feature self-integration network for geological lithology interpretation

Geological lithology interpretation in multi-source remote sensing imagery still faces challenges due to the spatial heterogeneity of multi-source features and the fragmented spatial distribution of geological elements. To address these issues, we propose the complementary feature self-integration network (CFSI-Net), a unified framework for multi-source feature fusion and lithology interpretation. To mitigate spatial heterogeneity in multi-source features, we developed the cross-domain feature extraction and fusion module (CDFEFM), a dual-branch structure that adaptively extracts and fuses complementary features across channels while reducing redundant lithology information. Furthermore, we introduced the frequency-spatial feature integration module (FSFIM) and a segmentation module (SegM) to alleviate the problem of boundary ambiguity in segmentation. FSFIM captures boundary structural details and spatial contextual dependencies from the frequency and spatial domains, whereas SegM further refines spatial dependencies across channels, enriching textural details and enhancing the discrimination of adjacent elements. Experimental results on the CUG-LithXZ dataset demonstrate that CFSI-Net consistently outperforms state-of-the-art models, achieving an OA of 80.13% and mIoU of 66.18% in Geo-GZ, and an OA of 72.49% and mIoU of 50.26% in Geo-LZ. These results confirm that the synergistic utilization of frequency and spatial information provides richer details for accurate geological lithology interpretation in complex terrains.