FusionAttNet Framework for Hierarchical Attention Driven Sentinel 1 and Sentinel 2 Fusion for Semi Arid Land Cover Classification in Far North Cameroon

Abstract Accurate land cover mapping in semi-arid regions remains challenging due to spectral homogeneity of sparse vegetation, seasonal variability, and persistent cloud cover. In this study, we propose a FusionAttNet, a novel deep learning framework integrating Sentinel-1 SAR and Sentinel-2 optical data through modality-aware hierarchical attention for land cover classification case study of Far North Cameroon. Our approach employs parallel sensor-specific processing streams (generating cross-modal indices like NDVI/VV ratios) and a three-tiered attention mechanism (pixel-patch-landscape) to resolve spatial ambiguities in semi-arid landscapes. Enhanced by modality-aware augmentation and focal loss with label smoothing, FusionAttNet achieved 96.75% overall accuracy and 0.9680 F1-score on a multi-seasonal dataset over a 12-month temporal window spanning the year 2021, outperforming feature-stacking (85.1%) and early fusion (87.6%) baselines. Key innovations include: (1) landscape-level attention capturing phenological transitions in Sahelian ecotones, (2) cross-modal indices mitigating cloud-induced optical data gaps, and (3) SAR-optimized augmentation preserving backscatter textures. Results demonstrate a 14.7% reduction in misclassification of mixed bare soil/grassland interfaces compared to state-of-the-art methods, establishing a new paradigm for semi-arid land cover monitoring.