Deep neural network for oil spill detection using Sentinel-1 data: application to Egyptian coastal regions

Abstract

Building an oil spill segmentation model is very challenging because of the limited available information on oil spill accidents. Therefore, this paper proposes a custom data generator based on Segmentation Network (Seg-Net) model implemented in Conditional Generative Adversarial Network (CGAN). The proposed model is trained for oil spill segmentation using 50 Sentinal-1 Synthetic Aperture Radar (SAR) images. The proposed model employs a modified Seg-Net as a generator to produce high-quality oil spills’ images and a Patch-GAN as discriminator. This architecture results in a significant improvement of the final oil segmentation results, in comparison with Seg-Net model, while using relatively small training dataset. For performance assessment, the paper presents the oil spills segmentation results of four suggested models using Sentinel-1 SAR images. The presented models are U-Net, Seg-Net, CGAN, and a Seg-Net-based CGAN the performance assessment reveals that the proposed model produces oil spill segmentation images with an average accuracy of 99.04%, Intersection over Union (IoU) index of 96.59%, and a precision of 85.24%. In addition, the training time required for the proposed model is 3 h 20 min per 50 epochs, while it is nearly 10 h and 55 min for training a CGAN model.

Keywords:

• Oil spills
• SAR
• deep learning
• segmentation
• generative adversarial network

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The dataset used in this analysis will be available to the public via https://drive.google.com/file/d/15WYzzFZvAHmqSIW0PXXRTp_YVd_868l8/view?usp=sharing