MS3OSD: A Novel Deep Learning Approach for Oil Spills Detection Using Optical Satellite Multisensor Spatial-Spectral Fusion Images

IF 4.7 2区 地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Kai Du;Yi Ma;Zhongwei Li;Rongjie Liu;Zongchen Jiang;Junfang Yang
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Abstract

Marine oil spills pose a significant threat to ecosystems, highlighting the critical need for effective monitoring technology. Optical remote sensing technology plays a crucial role in monitoring marine oil spills. However, its performance is constrained by inherent tradeoffs among temporal, spatial, and spectral resolutions, making it difficult for a single sensor to fully meet the demands of oil spill monitoring. Furthermore, existing oil spill detection algorithms often prioritize surrounding spatial features while neglecting the contribution of central spectral features, resulting in reduced detection accuracy. To address these issues, this article proposes a joint framework for multisensor data spatial-spectral fusion and oil spill detection. This framework fuse images from the coastal zone imager (50 m, 4 bands) with images from the ultraviolet imager and the Chinese Ocean Color and Temperature Scanner (1000 m, 10 bands), all of which are onboard Haiyang-1C/D satellites, generating high temporal and spatial resolution ultraviolet-visible-near-infrared range images with 10 bands. The framework uses parallel branches, including a convolutional neural network and a vision transformer, to extract surrounding spatial features and central spectral features from the fused data. This design enables the effective combination of fine-grained spatial information with multiband spectral information, facilitating precise detection of oil spills in various emulsification states under different sun glint conditions. The proposed framework demonstrates strong performance, achieving F1-scores of 95.24% and 93.04% for detecting oil slicks and oil emulsions under weak sun glint conditions, and 90.06% for positive contrast oil spills under strong sun glint conditions. This study provides new insights for advancing oil spill monitoring and highlights the potential of multisensor data fusion in marine target detection.
MS3OSD:一种新的基于光学卫星多传感器空间光谱融合图像的石油泄漏检测深度学习方法
海洋石油泄漏对生态系统构成重大威胁,迫切需要有效的监测技术。光学遥感技术在海洋溢油监测中起着至关重要的作用。然而,其性能受到时间、空间和光谱分辨率之间固有权衡的限制,使得单个传感器难以完全满足溢油监测的需求。此外,现有的溢油检测算法往往优先考虑周围的空间特征,而忽略了中心光谱特征的贡献,导致检测精度降低。为了解决这些问题,本文提出了一个多传感器数据空间光谱融合和溢油检测的联合框架。该框架将海洋- 1c /D卫星上搭载的50米4波段海岸带成像仪图像与紫外线成像仪和中国海洋色温扫描仪(1000米10波段)图像融合,生成10波段高时空分辨率紫外-可见-近红外图像。该框架使用并行分支,包括卷积神经网络和视觉转换器,从融合数据中提取周围空间特征和中心光谱特征。该设计能够将细粒度空间信息与多波段光谱信息有效结合,便于在不同太阳闪烁条件下对不同乳化状态下的溢油进行精确检测。该框架表现出较强的性能,在弱日照条件下检测浮油和油乳的f1得分分别为95.24%和93.04%,在强日照条件下检测正对比溢油的f1得分为90.06%。该研究为推进溢油监测提供了新的见解,并突出了多传感器数据融合在海洋目标检测中的潜力。
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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来源期刊
CiteScore
9.30
自引率
10.90%
发文量
563
审稿时长
4.7 months
期刊介绍: The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.
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