Detection of marine oil spills from radar satellite images for the coastal ecological risk assessment

SAGPNet: A shape-aware and adaptive strip self-attention guided progressive network for SAR marine oil spill detection

The oil spill is a significant source of marine pollution, causing severe harm to marine ecosystems. Detecting oil spills accurately using synthetic aperture radar (SAR) images is crucial for protecting the environment. However, oil spill targets in SAR images are small and resemble other objects "look-alike". Traditional semantic segmentation networks for MOSD may lose critical information during downsampling Hence, we propose a shape-aware and adaptive strip self-attention guided progressive network (SAGPNet) for MOSD. Firstly, we adopted the progressive strategy to reduce detailed information loss. Second, we improved the traditional U-Net by redesigning its encoder unit. Specifically, we proposed a shape-aware and multi-scale feature extraction module and an adaptive strip self-attention module (ASSAM). These modifications allow the model to extract shape, multi-scale, and global information during the encoding process, addressing the challenges posed by small targets and "look-alike". Third, we utilize the ASSAM to extract global features from the final encoding layer of the earlier stage of the progressive network to guide the encoding features of the subsequent stage, aiming to recognize the overall shape of the oil spill and ensure that the model preserves crucial contextual information, further mitigate the information loss caused by downsampling. Finally, we designed a joint loss to address pixel imbalance between oil spills and other targets. We use three public oil spill detection datasets to evaluate the performance of SAGPNet. The experimental results show superior performance compared to other state-of-the-art methods, highlighting the effectiveness of SAGPNet in addressing the challenges associated with MOSD.

Mapping of oil spills in China Seas using optical satellite data and deep learning

Oils spilled into the ocean can form various weathered oils (non-emulsified oil slicks (NEOS), oil emulsions (OE)) which threaten the oceanic and coastal environments and ecosystems. Optical remote sensing has the unique ability to discriminate oil types and quantify oil volumes as their spectral contrasts with oil-free seawater. Here, a deep learning-based model is developed for identification, classification, and quantification of various oil types. Based on the oil-contained datasets collected from 7 satellite sensors from April 2019 to August 2023, the origin, quantity, and spatial distribution of oils spilled from ships and rigs in the China Seas are mapped in detail. We found that oil spill incidents are primarily from ship discharges (85.8 %), while platform leaks lead to more oil emulsions (58.6 % compared to 13.1 % from ships), which illuminates that the drilling oils are the main source of oil spill pollution in China Seas. The spilled oils correlate with major port locations, including offshore Qingdao and Rongcheng, Bohai Bay, the adjacent areas of Beihai, and Hue and Danang in Vietnam. This study provides new insights into the assessment and management of offshore and marine oil spills.

MSMTRIU-Net: Deep Learning-Based Method for Identifying Rice Cultivation Areas Using Multi-Source and Multi-Temporal Remote Sensing Images

Identifying rice cultivation areas in a timely and accurate manner holds great significance in comprehending the overall distribution pattern of rice and formulating agricultural policies. The remote sensing observation technique provides a convenient means to monitor the distribution of rice cultivation areas on a large scale. Single-source or single-temporal remote sensing images are often used in many studies, which makes the information of rice in different types of images and different growth stages hard to be utilized, leading to unsatisfactory identification results. This paper presents a rice cultivation area identification method based on a deep learning model using multi-source and multi-temporal remote sensing images. Specifically, a U-Net based model is employed to identify the rice planting areas using both the Landsat-8 optical dataset and Sentinel-1 Polarimetric Synthetic Aperture Radar (PolSAR) dataset; to take full into account of the spectral reflectance traits and polarimetric scattering traits of rice in different periods, multiple image features from multi-temporal Landsat-8 and Sentinel-1 images are fed into the network to train the model. The experimental results on China's Sanjiang Plain demonstrate the high classification precisions of the proposed Multi-Source and Multi-Temporal Rice Identification U-Net (MSMTRIU-NET) and that inputting more information from multi-source and multi-temporal images into the network can indeed improve the classification performance; further, the classification map exhibits greater continuity, and the demarcations between rice cultivation regions and surrounding environments reflect reality more accurately.

A new ship tracing technology from oil spills based on multi-source data

Oil spill from ship can cause serious pollution to the Marine environment, but it is very difficult to find and confirm the troublemaker. In order to determine the oil spill ship, this paper proposes a new method to trace the source of ship oil spills and find the suspected ship that spills oil based on SAR imagery, AIS data and related marine environment data. First, we filter AIS data based on position of oil spill areas on remote sensing imagery and convert oil spill areas into trajectory points. Secondly, based on the Lagrangian particle motion model, a bidirectional drift model is proposed to calculate the average similarity between the forward and backward drift results. Finally, the most likely oil spill ship is determined according to the average similarity results. The results of the case study show that the method is effective and practical.

Marine oil spill detection and segmentation in SAR data with two steps Deep Learning framework

Marine oil spills pose significant ecological and economic threats worldwide, requiring effective decision-making tools. In this study, the optimal parameters, and configurations for Deep Learning models in oil spill classification and segmentation using Sentinel-1 SAR imagery were identified. First, a new Sentinel-1 image dataset was created. Ninety CNN configurations were explored for classification by varying the number of convolutional layers, filters, hidden layers, and neurons in each layer. For segmentation tasks, MLP and U-Net models were evaluated with variations in convolutional layers, filters, and incorporation of IoU and Focal Loss. The results indicated that a CNN model with six layers, 32 filters, and two hidden layers achieved 99 % classification accuracy. For segmentation, the U-Net model with more layers and filters using Focal Loss achieved 99 % accuracy and 96 % IoU. Therefore, a CNN and U-Net framework was proposed that achieves an overall accuracy of 95 % and an IoU of 90 %.

A data driven oil spill mapping using GMM clustering and damping ratio on X-Press Pearl ship disaster in the Indian Ocean

The work presented in this paper is focused on the largest marine disaster to have occurred in the Indian Ocean due to the breakup of the container tanker ship X-Press Pearl. In order to identify the oil spill and its temporal evolution, a recently proposed damping ratio (DR) index is employed. To derive the DR, a data-driven GMM-EM clustering method optimized by stochastic ordering of the resulting classes in Sentinel 1 SAR time series imagery is proposed. A ship-born oil spill site is essentially considered to consist of three subsites: oil, open sea, and ship. The initial site probability densities were determined by using k-means clustering. In addition to the clustering method, two histogram-based approaches, namely contextual peak thresholding (CPT) and contextual peak ordering (CPO), were also formulated and presented. The improved histogram peak detection methods take into account spatial and contextual dependencies. The similarity of the marginal probability densities of the open sea and the oil classes makes it difficult to quantify the DR values to show the level of dampening. In the study, we show that reasonable class separability to correctly determine the σVV0,seaθ is possible by using GMM clustering. Resulting class separability's are also reported using JM and ML distances. The methods tested show the range of derived DR values stays significantly within similar ranges to each other. The outcomes were tested with the ground-based surveys conducted during the disaster for oil spill sites and other chemical compounds. The proposed methods are simple to execute, robust, and fully automated. Further, they do not require masking the oil or the selection of high-confidence water pixels manually.

Automated marine oil spill detection algorithm based on single-image generative adversarial network and YOLO-v8 under small samples

As marine resources and transportation develop, oil spill incidents are increasing, endangering marine ecosystems and human lives. Rapidly and accurately identifying marine oil spill is of utmost importance in protecting marine ecosystems. Marine oil spill detection methods based on deep learning and computer vision have the great potential significantly enhance detection efficiency and accuracy, but their performance is often limited by the scarcity of real oil spill samples, posing a challenging to train a precise detection model. This study introduces a detection method specifically designed for scenarios with limited sample sizes. First, the small sample dataset of marine oil spill taken by Landsat-8 satellite is used as the training set. Then, a single image generative adversarial network (SinGAN) capable of training with a single oil spill image is constructed for expanding the dataset, generating diverse marine oil spill samples with different shapes. Second, a YOLO-v8 model is pretrained via the method of transfer learning and then trained with dataset before and after augmentation separately for real-time and efficient oil spill detection. Experimental results have demonstrated that the YOLO-v8 model, trained on an expanded dataset, exhibits notable enhancements in recall, precision, and average precision, with improvements of 12.3 %, 6.3 %, and 11.3 % respectively, compared to the unexpanded dataset. It reveals that our marine oil spill detection model based on YOLO-v8 exhibits leading or comparable performance in terms of recall, precision, and AP metrics. The data augmentation technique based on SinGAN contributes to the performance of other popular object detection algorithms as well.

APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion

As affected by limited information and the complex background, the accuracy of small-target water-floating garbage detection is low. To increase the detection accuracy, in this research, a small-target detection method based on APM-YOLOv7 (the improved YOLOv7 with ACanny PConv-ELAN and MGA attention) is proposed. Firstly, the adaptive algorithm ACanny (adaptive Canny) for river channel outline extraction is proposed to extract the river channel information from the complex background, mitigating interference of the complex background and more accurately extracting the features of small-target water-floating garbage. Secondly, the lightweight partial convolution (PConv) is introduced, and the partial convolution-efficient layer aggregation network module (PConv-ELAN) is designed in the YOLOv7 network to improve the feature extraction capability of the model from morphologically variable water-floating garbage. Finally, after analyzing the limitations of the YOLOv7 network in small-target detection, a multi-scale gated attention for adaptive weight allocation (MGA) is put forward, which highlights features of small-target garbage and decreases missed detection probability. The experimental results showed that compared with the benchmark YOLOv7, the detection accuracy in the form of the mean Average Precision (mAP) of APM-YOLOv7 was improved by 7.02%, that of mmAP (mAP0.5:0.95) was improved by 3.91%, and Recall was improved by 11.82%, all of which meet the requirements of high-precision and real-time water-floating garbage detection and provide reliable reference for the intelligent management of water-floating garbage.

Applications of bubble curtains in marine oil spill containment: Hydrodynamic characteristics, applications, and future perspectives

Although the marine oil spill pollution issue does not bring us to flock in droves as the new emerging oceanic techniques like wave energy converters, remote operated vehicle (ROV), blue ammonia and green hydrogen, the huge pollution risks of the marine oil spills caused by man-made intentional discharge, old equipment, accidental leakage, war and other aspects should arouse our sufficient attention and concern. As the primary countermeasure of emergency response to a marine oil spill, rapid & efficient oil containment is crucial to limit the pollution scope and the subsequent recovery and treatment. Here, we summarized the existing investigations on oil-spill containment with a marked emphasis on the applications of bubble curtains and their working mechanisms. The critical research progress and trends about the remediation techniques and the application of bubble curtains in marine environments were briefly introduced. The article thoroughly analyzed the basic working mechanism of the bubble curtains in oil spill containment, the technical difficulties of the existing methods, the potential application prospects of coupling with the traditional oil containment booms and the critical scientific problems to be studied in the future. Regarding the issues involving insufficient oil retention performance and inconvenient deployment of the existing traditional oil boom under complex and variable sea conditions, the performance and structural optimization of bubble curtain enhanced oil containment boom will get the top priority in developing the next-generation oil containment techniques.