3D-HRSCD: Exploiting the Potential of Multiscale Features by 3-D Convolution

Semantic change detection (SCD) in remote sensing image (RSI) is critical for monitoring land cover and land use transformations. Although existing SCD methods have made progress in modeling temporal dependency, they still struggle to effectively capture multi-scale features and make interaction among them. To address these issues, we propose 3D-HRSCD, a novel architecture that utilizes 3D convolution to model temporal dependency across HRNet’s multi-resolution features. The core of this architecture is 3D Convolution Fusion Oriented to Multi-scale Features (3DFOM) module, which makes adequate interaction in channel, spatial and temporal dimensions across multi-scale features. To support more efficient temporal dependency modeling in 3DFOM, Cosine Similarity-based Temporal Multi-Scales Attention (CTMA) module serves as a preprocessing stage by enhancing features in change regions. Additionally, Comprehensive Semantic Consistency (CSC) loss function is introduced to further suppress pseudo-changes and reduce semantic recognition errors. Experimental results reveal that our method outperforms state-of-the-art performances relative to previous SCD efforts.