Transfer learning and single-polarized SAR image preprocessing for oil spill detection

Nataliia Kussul , Yevhenii Salii , Volodymyr Kuzin , Bohdan Yailymov , Andrii Shelestov
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Abstract

This study addresses the challenge of oil spill detection using Synthetic Aperture Radar (SAR) satellite imagery, employing deep learning techniques to improve accuracy and efficiency. We investigated the effectiveness of various neural network architectures and encoders for this task, focusing on scenarios with limited training data. The research problem centered on enhancing feature extraction from single-channel SAR data to improve oil spill detection performance.
Our methodology involved developing a novel preprocessing pipeline that converts single-channel SAR data into a three-channel RGB representation. The preprocessing technique normalizes SAR intensity values and encodes extracted features into RGB channels.
Through an experiment, we have shown that a combination of the LinkNet with an EfficientNet-B4 is superior to pairs of other well-known architectures and encoders.
Quantitative evaluation revealed a significant improvement in F1-score of 0.064 compared to traditional dB-scale preprocessing methods. Qualitative assessment on independent SAR scenes from the Mediterranean Sea demonstrated better detection capabilities, albeit with increased sensitivity to look-alike.
We conclude that our proposed preprocessing technique shows promise for enhancing automatic oil spill segmentation from SAR imagery. The study contributes to advancing oil spill detection methods, with potential implications for environmental monitoring and marine ecosystem protection.
溢油检测的迁移学习和单极化SAR图像预处理
本研究解决了使用合成孔径雷达(SAR)卫星图像进行溢油检测的挑战,采用深度学习技术来提高准确性和效率。我们研究了各种神经网络架构和编码器在该任务中的有效性,重点关注训练数据有限的场景。研究问题集中在增强单通道SAR数据的特征提取,以提高溢油检测性能。我们的方法涉及开发一种新的预处理管道,将单通道SAR数据转换为三通道RGB表示。预处理技术对SAR强度值进行归一化处理,并将提取的特征编码到RGB通道中。通过实验,我们已经证明LinkNet与EfficientNet-B4的组合优于其他知名架构和编码器的组合。定量评价结果显示,与传统的db尺度预处理方法相比,f1得分显著提高0.064。对来自地中海的独立SAR场景的定性评估显示出更好的探测能力,尽管对相似物的灵敏度有所提高。我们的结论是,我们提出的预处理技术有望增强从SAR图像中自动分割溢油。该研究有助于改进溢油检测方法,对环境监测和海洋生态系统保护具有潜在的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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