溢油会带来严重的环境风险，因此早期检测对于有效响应和缓解至关重要。由于合成孔径雷达（Synthetic Aperture Radar，SAR）图像可在全天候条件下工作，基于SAR的溢油分割能够实现快速且稳健的监测。然而，在使用深度学习模型时，SAR溢油分割往往因标注数据稀缺而难以训练。为解决这一局限，我们提出了一种基于扩散并结合知识迁移的数据增强策略（diffusion-based data augmentation with knowledge transfer，DAKTer）。我们的DAKTer策略使扩散模型能够生成SAR溢油图像以及软标签对；与分割掩码（即硬标签）相比，软标签可提供更丰富的类别概率分布。此外，为了可靠地联合生成高质量SAR图像和良好对齐的软标签，我们引入了一个基于SNR的平衡因子，用于对齐扩散模型中两种模态的噪声破坏过程。利用生成的SAR图像和软标签，学生分割模型无需经过相同任务训练的教师模型即可学习稳健的特征表示，从而提升其分割溢油区域的能力。大量实验表明，我们的DAKTer策略能够有效地将逐像素类别概率知识迁移给学生分割模型，使其在SAR图像中区分溢油区域与其他相似区域。与其他生成式数据增强方法相比，我们的DAKTer策略可推动多种分割模型以大幅优势取得更优性能。
英文摘要：
Oil spills pose severe environmental risks, making early detection crucial for effective response and mitigation. As Synthetic Aperture Radar (SAR) images operate under all-weather conditions, SAR-based oil spill segmentation enables fast and robust monitoring. However, when using deep learning models, SAR oil spill segmentation often struggles in training due to the scarcity of labeled data. To address this limitation, we propose a diffusion-based data augmentation with knowledge transfer (DAKTer) strategy. Our DAKTer strategy enables a diffusion model to generate SAR oil spill images along with soft label pairs, which offer richer class probability distributions than segmentation masks (i.e. hard labels). Also, for reliable joint generation of high-quality SAR images and well-aligned soft labels, we introduce an SNR-based balancing factor aligning the noise corruption process of both modalilties in diffusion models. By leveraging the generated SAR images and soft labels, a student segmentation model can learn robust feature representations without teacher models trained for the same task, improving its ability to segment oil spill regions. Extensive experiments demonstrate that our DAKTer strategy effectively transfers the knowledge of per-pixel class probabilities to the student segmentation model to distinguish the oil spill regions from other look-alike regions in the SAR images. Our DAKTer strategy boosts various segmentation models to achieve superior performance with large margins compared to other generative data augmentation methods.
NORCE the Norwegian Research Center（挪威NORCE研究中心）；UiT the Arctic University of Norway（挪威北极大学）
We propose a deep learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. By means of a carefully designed neural network model for image segmentation trained on an extensive dataset, we are able to obtain state-of-the-art performance in oil spill detection, achieving results that are comparable to results produced by human operators. We also introduce a classification task, which is novel in the context of oil spill detection in SAR. Specifically, after being detected, each oil spill is also classified according to different categories…
Compositional Oil Spill Detection Based on Object Detector and Adapted Segment Anything Model from SAR Images
The Hong Kong Polytechnic University（香港理工大学）；Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University（香港理工大学可持续城市发展研究院）
OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model
Department of Computer Science, University of Exeter（计算机科学系，埃克塞特大学）；College of Oceanography and Space Informatics, China University of Petroleum (East China)（海洋科学与空间信息学院，中国石油大学（华东））
Facultad de Ingeniería, Universidad de Buenos Aires（布宜诺斯艾利斯大学工程学院）；Universidad Nacional de Tierra del Fuego, Antártida e Islas del Atlántico Sur（火地岛、南极和南大西洋岛屿国立大学）；Instituto Argentino de Matemática ”A. P. Calderón”, IAM, CONICET（阿根廷数学研究所“AP卡尔德隆”（IAM），阿根廷国家科学技术研究委员会（CONICET））；Instituto de Ciencias Polares, Ambiente y Recursos Naturales（极地、环境和自然资源研究所）
In this note we deal with the detection of oil spills in open sea via self similar, long range dependence random fields and wavelet filters. We show some preliminary experimental results of our technique with Sentinel 1 SAR images.
Enhancing Cross Domain SAR Oil Spill Segmentation via Morphological Region Perturbation and Synthetic Label-to-SAR Generation
Círculo de Investigación de Máquinas de Aprendizaje (CIMA), Universidad Nacional Agraria La Molina（机器学习研究圈（CIMA）、拉莫林农业国立大学）；Departamento de Estadística e Informática, Facultad de Economía y Planificación, Universidad Nacional Agraria La Molina（统计与信息技术系、经济与规划学院、拉莫林农业国立大学）
Kwame Nkrumah University of Science and Technology（夸梅·恩克鲁玛科技大学）
Synthetic Aperture Radar (SAR) imagery to monitor oil spills are some methods that have been proposed for the West African sub-region. With the increase in the number of oil exploration companies in Ghana (and her neighbors) and the rise in the coastal activities in the sub-region, there is the need for proper monitoring of the environmental impact of these socio-economic activities on the environment. Detection and near real-time information about oil spills are fundamental in reducing oil spill environmental impact. SAR images are prone to some noise, which is predominantly speckle noise aro…
Univ. Savoie Mont Blanc（萨瓦大学）；TotalEnergies S.E.（道达尔能源公司）
Ocean surface monitoring, especially oil slick detection, has become mandatory due to its importance for oil exploration and risk prevention on ecosystems. For years, the detection task has been performed manually by photo-interpreters using Synthetic Aperture Radar (SAR) images with the help of contextual data such as wind. This tedious manual work cannot handle the increasing amount of data collected by the available sensors and thus requires automation. Literature reports conventional and semi-automated detection methods that generally focus either on oil slicks originating from anthropogen…
Synthetic Aperture Radar (SAR) is the main instrument utilized for the detection of oil slicks on the ocean surface. In SAR images, some areas affected by ocean phenomena, such as rain cells, upwellings, and internal waves, or discharge from oil spills appear as dark spots on images. Dark spot detection is the first step in the detection of oil spills, which then become oil slick candidates. The accuracy of dark spot segmentation ultimately affects the accuracy of oil slick identification. Although some advanced deep learning methods that use pixels as processing units perform well in remote s…
Idea-re S.r.l.（Idea-re有限责任公司）；Guglielmo Marconi University（古列尔莫·马可尼大学）；Alma Mater Studiorum Università di Bologna（博洛尼亚大学）
In this work, we describe in detail how Deep Learning and Computer Vision can help to detect fault events of the AirTender system, an aftermarket motorcycle damping system component. One of the most effective ways to monitor the AirTender functioning is to look for oil stains on its surface. Starting from real-time images, AirTender is first detected in the motorbike suspension system, simulated indoor, and then, a binary classifier determines whether AirTender is spilling oil or not. The detection is made with the help of the Yolo5 architecture, whereas the classification is carried out with …
Synthetic Aperture Radar (SAR) constitutes a fundamental asset for wide-areas monitoring with high-resolution requirements. The first SAR sensors have given rise to coarse coastal and maritime monitoring applications, including oil spill, ship and ice floes detection. With the upgrade to very high-resolution sensors in the recent years, with relatively new SAR missions such as Sentinel-1, a great deal of data providing a stronger information content has been released, enabling more refined studies on general targets features and thus permitting complex classifications, as for ship classificati…
In this work, we present a methodology for monitoring man-made, construction-like activities in low-resolution SAR imagery. Our source of data is the European Space Agency Sentinel-l satellite which provides global coverage at a 12-day revisit rate. Despite limitations in resolution, our methodology enables us to monitor activity levels (i.e. presence of vehicles, equipment) of a pre-defined location by analyzing the texture of detected SAR imagery. Using an exploratory dataset, we trained a support vector machine (SVM), a random binary forest, and a fully-connected neural network for classifi…
SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation
College of Instrumentation and Electrical Engineering, Jilin University（吉林大学仪器电气工程学院）；Key Laboratory of Geophysical Exploration Equipment, Ministry of Education（教育部地球物理探测装备重点实验室）；School of Mathematics, Jilin University（吉林大学数学学院）
