An improved semantic segmentation model based on SVM for marine oil spill detection using SAR image

Area of Science:

• Remote Sensing
• Marine Biology
• Artificial Intelligence

Background:

• Oil spills pose significant threats to marine ecosystems, necessitating rapid and accurate detection methods.
• Synthetic Aperture Radar (SAR) offers all-weather, all-time imaging capabilities, providing rich polarimetric data crucial for oil spill identification.
• Current semantic segmentation models face challenges in classifier performance, limiting their effectiveness in oil spill recognition.

Purpose of the Study:

• To propose an improved semantic segmentation model, DRSNet, for enhanced oil spill detection using SAR imagery.
• To address the limitations in classifier performance within existing semantic segmentation frameworks for oil spill identification.
• To leverage polarimetric features from SAR data for more robust oil spill recognition.

Main Methods:

• Developed DRSNet, an enhanced semantic segmentation model integrating ResNet-50 as the backbone within the DeepLabv3+ architecture.
• Employed Support Vector Machines (SVM) as the classifier in the DRSNet model.
• Utilized ten distinct polarimetric features extracted from SAR images for model training and evaluation.

Main Results:

• The proposed DRSNet model demonstrated superior performance compared to other semantic segmentation models in oil spill identification tasks.
• Experiments confirmed the effectiveness of DRSNet in accurately recognizing oil spills using multi-feature SAR data.
• The integration of ResNet-50 and SVM within the DeepLabv3+ framework significantly boosted recognition accuracy.

Conclusions:

• The DRSNet model offers a valuable advancement for improving oil spill detection capabilities using SAR imagery.
• This research provides a robust tool to enhance maritime emergency management and environmental protection efforts.
• The findings highlight the potential of advanced deep learning techniques combined with polarimetric SAR data for marine environmental monitoring.