Boosting Crater Detection via ViT-Based Feature Fusion From Near-IR Images and DEMs

Inspired by the recent progress of multimodal fusion in a variety of computer vision tasks, this letter aims to propose a two-stream fusion crater detection network (TFCDNet). Toward this end, near-infrared (IR) images and digital elevation maps (DEMs) in the feature domain are appropriately fused to boost the performance of crater detection (CD). The proposed TFCDNet includes a powerful feature-coding module that can effectively extract and fuse multimodal features. The comprehensively conducted experiments on both optical-DEM paired lunar crater detection dataset (ODPLCD) and Mars day CD (MDCD) datasets reveal that the proposed TFCDNet is capable of being more competitive than the state of the arts. As a result, this work is anticipated to spark some new thinking in CD.