Dynamic Spatial Feature Enhancement for Local Climate Zone Classification in SAR and Multi-Spectral Data

Local Climate Zone (LCZ) classification from remote sensing images plays a crucial role in quantifying the urban heat island effect. However, the performance of LCZ classification has not been satisfactory so far, especially for built-up area categories. To alleviate this issue, we introduce a novel network architecture, DS-LCZNET, which incorporates a Dynamic Spatial Feature Enhancement (DSFE) module for capturing complex spatial information and a SAR-MS Fusion (SMF) module to improve feature integration from SAR and MS data. Extensive experiments demonstrate that DS-LCZNET significantly enhances classification performance, achieving a 3.55% increase in overall accuracy, a 1.18% improvement in average accuracy (AA), and a 3.88% rise in the kappa (x100) coefficient compared to the current leading baseline, MsF- LCZ-Net. The codes will be publicly available at: https://github.com/zhyilin97/DSLCZNET.