CBENet: contextual and boundary-enhanced network for oil spill detection via microwave remote sensing

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Article title: "CBENet: contextual and boundary-enhanced network for oil spill detection via microwave remote sensing"
Authors: Mengmeng Di , Xinnan Di , Huiyao Xiao , Ying Gao and Yongqing Li
Source: https://doi.org/10.1007/s44295-025-00056-5
Date: 27 February 2025

Summary and Introduction

Oil spills can be a major issue in marine environments, which is why it is important to be able to detect them early. Detection typically entails the use of microwave remote sensing images, specifically synthetic aperture radar (SAR). This technique uses radar pulses from aircraft and can identify oil spills with high-resolution imagery, nonstop, and is resistant to weather conditions such as precipitation and fog. SAR is often paired with deep learning models to increase its effectiveness. The researchers listed various models that were tested in previous studies, as well as the issues they overcame. Some of the technologies they mentioned are deep convolutional neural networks (DCNNs), U-shaped networks, multiscale conditional adversarial network (MCAN), generative adversarial networks (GANs), and many other models and methodologies for detecting oil spills. This paper created a contextual and boundary-enhanced network (CBENet) to analyze the SAR images. The goal of this technique is to address certain issues regarding the quality of the SAR images. For example, the images don’t identify oil spills well at varied scales. Additionally, boundaries of the spills are often blurry, affecting the accuracy of detecting them. The paper finds that CBENet is an effective technique and was validated via various qualitative and quantitative evaluations.