A satellite-based deep learning system that detects and segments oil spills from radar imagery.
A U-Net with a ResNet-34 encoder captures spatial and contextual features from dual-polarization SAR data, trained with a hybrid Dice, Focal and BCE loss to handle severe class imbalance. Post-processing reduces false positives from noise and look-alike phenomena, reaching 98.28% accuracy and 70.4% IoU in a Streamlit deployment.
Oil spills must be found fast and in any weather, which rules out optical imagery. SAR works, but spills occupy a tiny fraction of each scene and look confusingly similar to wind shadows and algal slicks.
Segment rather than classify, use a loss combination built for extreme class imbalance, and add a post-processing stage specifically targeting the look-alike phenomena that generate false alarms.
U-Net + ResNet-34 Segmentation
Captures spatial and contextual features from dual-polarization SAR data.
Hybrid Loss Function
Combines Dice, Focal, and BCE loss to address class imbalance.
Post-Processing Pipeline
Reduces false positives from noise and look-alike phenomena.
Streamlit Deployment
Real-time inference and visualization; 98.28% accuracy, 70.4% IoU.
Deep Learning
Remote sensing
Application
SAR ingestion
Dual-polarization radar scenes are loaded and tiled.
Segmentation
U-Net predicts spill masks pixel-wise.
Loss-balanced training
Hybrid loss counters extreme class imbalance.
Post-processing
Look-alike false positives are filtered out.
Visualisation
Masks and metrics are displayed in real time.
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