πŸš€ V4: TÍCH Hα»’P RADAR SENTINEL-1 (32-CHANNELS FUSION) ============================================================ Clean FUSION data: (252, 32, 16, 16), 7 classes, [31, 38, 32, 49, 23, 75, 4] ============================================================ 32-CHANNELS FUSION CNN ============================================================ Ep 1 Fusion-Acc=0.1961 🌟 Ep 3 Fusion-Acc=0.2745 🌟 Ep 4 Fusion-Acc=0.4706 🌟 Ep 5 Fusion-Acc=0.5490 🌟 Ep 6 Fusion-Acc=0.7059 🌟 Ep 7 Fusion-Acc=0.7451 🌟 Ep 8 Fusion-Acc=0.8431 🌟 Ep 15 Fusion-Acc=0.8627 🌟 βœ… CNN Fusion best: 0.8627 ============================================================ HYBRID FUSION: CNN embed + S1/S2 Rich features + XGBoost ============================================================ Extracted 2182 fusion features per sample Final Feature Vector: (252, 2694) βœ… Hybrid Fusion Acc: 0.8627 Fold 1: 0.9412 Fold 2: 0.9020 Fold 3: 0.9200 Fold 4: 0.8800 Fold 5: 0.9000 βœ… CV Mean: 0.9086 Β± 0.0206 ============================================================ πŸ“Š FINAL RESULTS V4 (WITH RADAR) ============================================================ βœ… Hybrid Fusion CV: 0.9086 πŸ“ˆ CNN Fusion (32ch): 0.8627 πŸ“ˆ Hybrid Fusion (CNN+XGB): 0.8627 πŸ† BEST: 0.9086