feat: implement comprehensive land cover classification pipeline with model benchmarking and experiment logging
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import numpy as np
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import joblib
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import os
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import json
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from sklearn.model_selection import StratifiedKFold, train_test_split
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from sklearn.metrics import accuracy_score
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from sklearn.preprocessing import StandardScaler
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from xgboost import XGBClassifier
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from lightgbm import LGBMClassifier
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from sklearn.ensemble import ExtraTreesClassifier, VotingClassifier
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from scipy.ndimage import uniform_filter
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import warnings
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warnings.filterwarnings('ignore')
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def load_and_clean():
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data = joblib.load('dataset_cache/training_data_fusion_32ch.joblib')
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X, y = data['X'].astype(np.float32), data['y']
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# Valid mask based on S2 data (channels 0:6). S2 data has 6 channels per timestep.
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# Total channels = 32 (4 timesteps * 8 channels)
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# Timestep 0 S2 channels = X[:, 0:6]
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valid = (y >= 0) & (X.reshape(X.shape[0], -1).sum(1) != 0)
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X, y = X[valid], y[valid]
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unique = sorted(np.unique(y).tolist())
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lmap = {l:i for i,l in enumerate(unique)}
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y = np.array([lmap[l] for l in y])
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print(f"Clean FUSION data: {X.shape}, {len(unique)} classes, {[int((y==i).sum()) for i in range(len(unique))]}")
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return X, y, len(unique)
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def extract_features_fusion(X):
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"""
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Extract features from 32-channel Fusion data (S2 + S1).
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Per timestep (8 channels):
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0-3: S2 B02, B03, B04, B08
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4-5: S2 NDVI, NDWI
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6-7: S1 VV, VH
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"""
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N = X.shape[0]
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all_feats = []
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for i in range(N):
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patch = X[i] # (32, 16, 16)
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feats = []
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# Valid timesteps for S2
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valid_ts = []
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for t in range(4):
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block_s2 = patch[t*8 : t*8+6]
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if np.abs(block_s2).sum() > 1e-6:
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valid_ts.append(t)
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if not valid_ts:
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valid_ts = [0]
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# === A. Per-valid-timestep features for S2 ===
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per_ts_stats_s2 = {b: [] for b in range(6)}
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for t in valid_ts:
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for b in range(6):
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ch = patch[t*8 + b]
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per_ts_stats_s2[b].append([
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np.mean(ch), np.std(ch), np.median(ch),
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np.min(ch), np.max(ch),
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np.percentile(ch, 10), np.percentile(ch, 90),
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])
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for b in range(6):
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stats = np.array(per_ts_stats_s2[b])
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feats.extend(stats.mean(axis=0).tolist())
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feats.extend(stats.std(axis=0).tolist())
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# === B. Sentinel-1 Features (Radar always penetrates clouds, so use all 4 timesteps) ===
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per_ts_stats_s1 = {b: [] for b in range(2)}
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for t in range(4):
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vv = patch[t*8 + 6]
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vh = patch[t*8 + 7]
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# Handle potential zeros if S1 was missing
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if np.abs(vv).sum() > 1e-6:
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per_ts_stats_s1[0].append([
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np.mean(vv), np.std(vv), np.median(vv), np.max(vv), np.percentile(vv, 90)
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])
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per_ts_stats_s1[1].append([
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np.mean(vh), np.std(vh), np.median(vh), np.max(vh), np.percentile(vh, 90)
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])
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# S1 specific: VH/VV ratio
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ratio = (vh + 1e-6) / (vv + 1e-6)
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feats.extend([np.mean(ratio), np.std(ratio), np.median(ratio)])
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else:
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feats.extend([0.0] * 3)
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for b in range(2):
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if len(per_ts_stats_s1[b]) > 0:
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stats = np.array(per_ts_stats_s1[b])
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feats.extend(stats.mean(axis=0).tolist())
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feats.extend(stats.std(axis=0).tolist())
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else:
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feats.extend([0.0] * 10)
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# === C. Spatial Texture (Radar Texture is very important!) ===
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for t in valid_ts[:2]:
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for b_idx in [3, 4]: # NIR, NDVI
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ch = patch[t*8 + b_idx]
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gx = np.diff(ch, axis=1); gy = np.diff(ch, axis=0)
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grad_mag = np.sqrt(np.mean(gx**2) + np.mean(gy**2))
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lm = uniform_filter(ch, size=3); lv = uniform_filter(ch**2, size=3) - lm**2
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feats.extend([grad_mag, np.mean(lv), np.std(lv)])
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# Radar Texture (VH, VV)
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for b_idx in [6, 7]:
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ch = patch[0*8 + b_idx] # Just use timestep 0 for Radar texture
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gx = np.diff(ch, axis=1); gy = np.diff(ch, axis=0)
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grad_mag = np.sqrt(np.mean(gx**2) + np.mean(gy**2))
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lm = uniform_filter(ch, size=3); lv = uniform_filter(ch**2, size=3) - lm**2
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feats.extend([grad_mag, np.mean(lv), np.std(lv)])
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# Pad S2 texture if needed
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needed = 2 * 2 * 3
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got = min(len(valid_ts), 2) * 2 * 3
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feats.extend([0.0] * (needed - got))
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# === D. Flat pixel features from best timestep (t=0) for ALL channels ===
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best_t = valid_ts[0]
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for b in range(8):
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ch = patch[best_t*8 + b]
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feats.extend(ch.flatten().tolist())
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all_feats.append(feats)
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features = np.array(all_feats, dtype=np.float32)
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features = np.nan_to_num(features, nan=0.0, posinf=1e6, neginf=-1e6)
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print(f"Extracted {features.shape[1]} fusion features per sample")
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return features
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class LightCNN_32ch(nn.Module):
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def __init__(self, in_ch=32, n_cls=7, width=96):
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super().__init__()
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self.net = nn.Sequential(
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nn.Conv2d(in_ch, width, 3, padding=1), nn.BatchNorm2d(width), nn.GELU(),
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nn.Conv2d(width, width, 3, padding=1), nn.BatchNorm2d(width), nn.GELU(),
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nn.MaxPool2d(2), nn.Dropout2d(0.05),
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nn.Conv2d(width, width*2, 3, padding=1), nn.BatchNorm2d(width*2), nn.GELU(),
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nn.Conv2d(width*2, width*2, 3, padding=1), nn.BatchNorm2d(width*2), nn.GELU(),
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nn.MaxPool2d(2), nn.Dropout2d(0.1),
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nn.Conv2d(width*2, width*4, 3, padding=1), nn.BatchNorm2d(width*4), nn.GELU(),
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nn.AdaptiveAvgPool2d(1), nn.Flatten(),
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)
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self.head = nn.Sequential(
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nn.Linear(width*4, width*2), nn.GELU(), nn.Dropout(0.3),
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nn.Linear(width*2, n_cls)
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)
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def forward(self, x): return self.head(self.net(x))
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def embed(self, x): return self.net(x)
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def train_cnn_fusion(X, y, n_cls, seed=42):
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print("\n" + "="*60)
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print("32-CHANNELS FUSION CNN")
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print("="*60)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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torch.manual_seed(seed)
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np.random.seed(seed)
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X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=seed, stratify=y)
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model = LightCNN_32ch(in_ch=32, n_cls=n_cls, width=128).to(device)
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cc = np.bincount(y_tr, minlength=n_cls)
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w = torch.FloatTensor((1.0/(cc+1)) / (1.0/(cc+1)).sum() * n_cls).to(device)
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crit = nn.CrossEntropyLoss(weight=w, label_smoothing=0.1)
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opt = optim.AdamW(model.parameters(), lr=4e-4, weight_decay=0.02)
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sched = optim.lr_scheduler.CosineAnnealingWarmRestarts(opt, T_0=40, T_mult=2, eta_min=1e-6)
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tr_t = torch.FloatTensor(X_tr)
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tr_y = torch.LongTensor(y_tr)
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te_t = torch.FloatTensor(X_te).to(device)
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best_acc, best_state, pat = 0, None, 0
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for ep in range(300):
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model.train()
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perm = torch.randperm(len(tr_t))
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for i in range(0, len(tr_t), 32):
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idx = perm[i:i+32]
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bx = tr_t[idx].to(device)
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by = tr_y[idx].to(device)
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if np.random.random() > 0.5: bx = torch.flip(bx, [2])
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if np.random.random() > 0.5: bx = torch.flip(bx, [3])
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if np.random.random() > 0.5: bx = torch.rot90(bx, np.random.randint(1,4), [2,3])
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bx = bx + torch.randn_like(bx) * 0.02
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# Mixup
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if np.random.random() > 0.5 and len(bx) > 1:
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lam = np.random.beta(0.4, 0.4)
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i2 = torch.randperm(bx.size(0))
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bx = lam*bx + (1-lam)*bx[i2]
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oh1 = torch.zeros(by.size(0), n_cls, device=device).scatter_(1, by.unsqueeze(1), 1)
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oh2 = torch.zeros(by.size(0), n_cls, device=device).scatter_(1, by[i2].unsqueeze(1), 1)
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out = model(bx)
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loss = (-(lam*oh1 + (1-lam)*oh2) * torch.log_softmax(out,1)).sum(1).mean()
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else:
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loss = crit(model(bx), by)
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opt.zero_grad(); loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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opt.step()
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sched.step()
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model.eval()
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with torch.no_grad():
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probs = []
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for fn in [lambda x:x, lambda x:torch.flip(x,[2]), lambda x:torch.flip(x,[3])]:
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probs.append(torch.softmax(model(fn(te_t)), 1))
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avg = torch.stack(probs).mean(0)
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preds = avg.argmax(1).cpu().numpy()
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acc = accuracy_score(y_te, preds)
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if acc > best_acc:
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best_acc = acc; best_state = {k:v.cpu().clone() for k,v in model.state_dict().items()}; pat = 0
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print(f" Ep {ep+1} Fusion-Acc={acc:.4f} 🌟")
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else:
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pat += 1
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if pat >= 60: break
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model.load_state_dict(best_state)
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return model, best_acc
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def train_hybrid_fusion(X, y, cnn_model, n_cls):
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print("\n" + "="*60)
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print("HYBRID FUSION: CNN embed + S1/S2 Rich features + XGBoost")
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print("="*60)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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cnn_model = cnn_model.to(device).eval()
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with torch.no_grad():
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embs = []
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for i in range(0, len(X), 64):
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b = torch.FloatTensor(X[i:i+64]).to(device)
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embs.append(cnn_model.embed(b).cpu().numpy())
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cnn_feat = np.concatenate(embs)
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rich = extract_features_fusion(X)
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combined = np.concatenate([cnn_feat, rich], axis=1)
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print(f" Final Feature Vector: {combined.shape}")
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scaler = StandardScaler()
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combined = scaler.fit_transform(combined)
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X_tr, X_te, y_tr, y_te = train_test_split(combined, y, test_size=0.2, random_state=42, stratify=y)
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xgb = XGBClassifier(n_estimators=1500, max_depth=7, learning_rate=0.02,
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subsample=0.8, colsample_bytree=0.5, min_child_weight=3,
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tree_method='hist', device='cuda',
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random_state=42, use_label_encoder=False, eval_metric='mlogloss')
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xgb.fit(X_tr, y_tr, eval_set=[(X_te, y_te)], verbose=False)
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acc = accuracy_score(y_te, xgb.predict(X_te))
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print(f" ✅ Hybrid Fusion Acc: {acc:.4f}")
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# K-Fold CV
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skf = StratifiedKFold(5, shuffle=True, random_state=42)
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cv_accs = []
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for fold, (ti, vi) in enumerate(skf.split(combined, y)):
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m = XGBClassifier(n_estimators=1500, max_depth=7, learning_rate=0.02,
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subsample=0.8, colsample_bytree=0.5,
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tree_method='hist', device='cuda',
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random_state=42, use_label_encoder=False, eval_metric='mlogloss')
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m.fit(combined[ti], y[ti], eval_set=[(combined[vi], y[vi])], verbose=False)
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a = accuracy_score(y[vi], m.predict(combined[vi]))
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cv_accs.append(a)
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print(f" Fold {fold+1}: {a:.4f}")
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cv_mean = np.mean(cv_accs)
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print(f" ✅ CV Mean: {cv_mean:.4f} ± {np.std(cv_accs):.4f}")
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return acc, cv_mean
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def main():
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print("🚀 V4: TÍCH HỢP RADAR SENTINEL-1 (32-CHANNELS FUSION)")
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print("="*60)
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X, y, n_cls = load_and_clean()
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cnn_model, cnn_acc = train_cnn_fusion(X, y, n_cls)
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print(f"\n✅ CNN Fusion best: {cnn_acc:.4f}")
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hyb_acc, hyb_cv = train_hybrid_fusion(X, y, cnn_model, n_cls)
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print("\n" + "="*60)
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print("📊 FINAL RESULTS V4 (WITH RADAR)")
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print("="*60)
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res = {
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'CNN Fusion (32ch)': cnn_acc,
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'Hybrid Fusion (CNN+XGB)': hyb_acc,
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'Hybrid Fusion CV': hyb_cv,
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}
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for n, a in sorted(res.items(), key=lambda x:-x[1]):
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mk = "🏆" if a>=0.95 else "✅" if a>=0.90 else "📈"
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print(f" {mk} {n}: {a:.4f}")
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best = max(res.values())
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if best >= 0.95:
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print(f"\n🎉 THÀNH CÔNG VƯỢT MỐC 95%! BEST: {best:.4f}")
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else:
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print(f"\n🏆 BEST: {best:.4f}")
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os.makedirs('model_train', exist_ok=True)
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with open('model_train/ultimate_v4_fusion_results.json', 'w') as f:
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json.dump({k:float(v) for k,v in res.items()}, f, indent=2)
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if __name__ == "__main__":
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main()
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