BO-DRNet: An Improved Deep Learning Model for Oil Spill Detection by Polarimetric Features from SAR Images

Dawei Wang,Yanlong Chen,Shanwei Liu,Mingming Xu,Jianhua Wan,Peng Ren,Muhammad Yasir

doi:10.3390/rs14020264

Abstract

Oil spill pollution at sea causes significant damage to marine ecosystems. Quad-polarimetric Synthetic Aperture Radar (SAR) has become an essential technology since it can provide polarization features for marine oil spill detection. Using deep learning models based on polarimetric features, oil spill detection can be achieved. However, there is insufficient feature extraction due to model depth, small reception field lend due to loss of target information, and fixed hyperparameter for models. The effect of oil spill detection is still incomplete or misclassified. To solve the above problems, we propose an improved deep learning model named BO-DRNet. The model can obtain a more sufficiently and fuller feature by ResNet-18 as the backbone in encoder of DeepLabv3+, and Bayesian Optimization (BO) was used to optimize the model’s hyperparameters. Experiments were conducted based on ten prominent polarimetric features were extracted from three quad-polarimetric SAR images obtained by RADARSAT-2. Experimental results show that compared with other deep learning models, BO-DRNet performs best with a mean accuracy of 74.69% and a mean dice of 0.8551. This paper provides a valuable tool to manage upcoming disasters effectively.

Highlights

With the development of the world economy, more and more international trade is completed by marine transportation
Gulf of Mexico (GOM) on 20 April 2010, a large amount of crude oil was released into the GOM, which presented a significant threat to the coastline and the living marine resources of the GOM [3]
Three quad-polarimetric oil spill Synthetic Aperture Radar (SAR) images were analyzed obtained by RADARSAT-2 over the Gulf of Mexico

Summary

Introduction

With the development of the world economy, more and more international trade is completed by marine transportation. Large cargo ships and oil tankers are busy shuttling through major ports, increasing marine oil spill risk. More than about 53% of marine oil spill are caused by leaks, transportation, and utilization of petroleum [1]. Oil spills are a global problem, causing serious effects on the ocean ecological environment, which can take decades to recover [2]. In the Deepwater Horizon oil spill accident in the. Gulf of Mexico (GOM) on 20 April 2010, a large amount of crude oil was released into the GOM, which presented a significant threat to the coastline and the living marine resources of the GOM [3]. Detecting marine oil spills quickly and accurately is significant

Results

Discussion

Conclusion