HAFNet : A Heterogeneous Adaptive Fusion Network of Optical and SAR Imagery for Improved Land Use Classification

ABSTRACT The interpretation of high‐resolution remote sensing imagery is essential for accurate land use classification. Optical and synthetic aperture radar (SAR) imagery exhibit complementary characteristics. Their fusion offers an effective approach to mitigating the limitations of single‐modal data and improving classification performance. However, the modal heterogeneity and complexity of optical and SAR imagery pose significant challenges for effective fusion. To address these issues, we propose a heterogeneous adaptive fusion network (HAFNet). First, the multi‐modality feature extractor leverages HRNet to retain the spatial details and local textures of optical images, while a lightweight SparseDense Transformer captures the global structural patterns of SAR data. Within M2FE, the multi‐branch integrated feature enhancement further strengthens the extracted features by emphasizing essential semantic information and suppressing noise. Second, the adaptive multi‐scale attention fusion module employs multi‐scale channel and spatial attention to capture critical information from both modalities, and incorporates a gating mechanism to adjust fusion weights, thereby dynamically exploiting cross‐modal complementarity. Finally, the U‐shaped framework with skip connections enhanced by a dynamic channel fusion module restores spatial resolution and improves the recognition accuracy of small‐scale land cover categories. To validate HAFNet, we conducted extensive comparisons with state‐of‐the‐art models on three public datasets with different resolutions. Experimental results demonstrate that HAFNet achieves improvements of approximately 1.2% in overall accuracy, 1.3% in the Kappa coefficient, 1.4% in F1‐score, and 1.6% in mean Intersection over Union, confirming its effectiveness in land use classification.