SAR-Based Marine Oil Spill Detection Using the DeepSegFusion Architecture

TL;DR

This paper introduces DeepSegFusion, a hybrid deep learning model combining SegNet and DeepLabV3+ with attention mechanisms, significantly improving accuracy and reducing false alarms in SAR satellite oil spill detection.

Contribution

The paper presents a novel hybrid deep learning architecture with attention-based feature fusion for enhanced oil spill segmentation in SAR images, outperforming traditional methods.

Findings

Achieved 94.85% accuracy in oil spill detection.

Reduced false detections by over 64%.

Demonstrated stable performance across various marine conditions.

Abstract

Detection of oil spills from satellite images is essential for both environmental surveillance and maritime safety. Traditional threshold-based methods frequently encounter performance degradation due to very high false alarm rates caused by look-alike phenomena such as wind slicks and ship wakes. Here, a hybrid deep learning model, DeepSegFusion, is presented for oil spill segmentation in Synthetic Aperture Radar (SAR) images. The model uses SegNet and DeepLabV3+ integrated with an attention-based feature fusion mechanism to achieve better boundary precision as well as improved contextual understanding. Results obtained on SAR oil spill datasets, including ALOS PALSAR imagery, confirm that the proposed DeepSegFusion model achieves an accuracy of 94.85%, an Intersection over Union (IoU) of 0.5685, and a ROC-AUC score of 0.9330. The proposed method delivers more than three times fewer false detections compared to individual baseline models and traditional non-segmentation methods, achieving a reduction of 64.4%. These results indicate that DeepSegFusion is a stable model under various marine conditions and can therefore be used in near real-time oil spill monitoring scenarios.