A Two-Stage Oil Spill Detection Method Based on an Improved Superpixel Module and DeepLab V3+ Using SAR Images

The application of deep learning in synthetic aperture radar (SAR) oil spill detection often faces challenges such as speckle noise and limited data volume. To address these issues, this article proposes a two-stage oil spill detection method, SD-OIL, which consists of a superpixel generation module (S3G), and a semantic segmentation model, DeepLab V3+ (the implementation process can be seen at https://github.com/GeminiCheng/ResearchCode). The first stage emphasizes superpixel generation, where S3G innovatively employs social support analysis and spectral angle mapping to develop a pixel-based social support quantification model that considers both individual and community perspectives, facilitating effective superpixel generation. In the semantic segmentation stage, the output from S3G enhances the segmentation performance of DeepLab V3+. Experimental results show that SD-OIL surpasses numerous existing segmentation-based oil spill detection methods, achieving an mIoU of 91.69%. The results also indicate that the S3G module significantly improves the accuracy of oil spill detection.

I. Introduction

Synthetic aperture radar (SAR) is one of the most effective remote sensing methods for oil spill detection [1]. As an active microwave sensor, SAR operates under all-weather and all-day conditions, making it highly suitable for Earth surface observation, as it is not dependent on solar radiation like optical imagery [2]. Oil spills alter the roughness of the sea surface by suppressing capillary and short gravity waves, which affects Bragg scattering and reduces backscatter intensity. Consequently, oil spills typically appear as dark spots in SAR images [3]. Traditional SAR oil spill detection consists of three stages: 1) dark spot extraction; 2) feature extraction; and 3) classification.