Oil Spill Detection in Synthetic Aperture Radar Images Using Lipschitz-Regularity and Multiscale Techniques

Ajadi, Olaniyi A.; Meyer, Franz J.; Tello, M.; Ruello, Giuseppe

doi:10.1109/jstars.2018.2827996

Cited by 17 publications

(4 citation statements)

References 43 publications

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“…ML models are developed to solve complex classification problems through recursive and iterative analysis of candidate solutions from given training samples and features without explicitly being programmed to do the task [233]. Various classification algorithms, such as artificial neural network (ANN) [50,52,141,146,163,199,207], SVM [145,180], decision tree (DT) [177], K-nearest neighbor [48,64], genetic algorithm [123,127,130], random forest (RF) [26], fuzzy logic [109,135,136,138], maximum likelihood [234], linear discriminant analysis [114,194], k-means [119], Mahalanobis distance [113], naïve Bayes [110], ensemble learning [46,115], Classification and Regression Trees (CART) [132], and others [38,51,140,142,235,236], have been used to classify oil spills and lookalikes. Widely used traditional ML classification model for oil detection from optical and SAR images are listed in Table 4.…”

Section: Traditional Machine Learning Techniquesmentioning

confidence: 99%

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

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Section: Traditional Machine Learning Techniquesmentioning

confidence: 99%

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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“…This technology utilizes synthetic aperture radar carried by satellites to efficiently detect oil films on the ocean surface, without being limited by weather or lighting conditions [22,23]. SAR satellite remote sensing has numerous advantages compared to technologies such as hyperspectral remote sensing, visible light remote sensing, and thermal infrared remote sensing [24][25][26]. Firstly, SAR satellites utilize radar beams to transmit and receive electromagnetic waves, unaffected by weather and lighting conditions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing

Li,

Yang,

Yang

et al. 2024

Remote Sensing

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The efficient and timely identification of oil spill areas is crucial for ocean environmental protection. Synthetic aperture radar (SAR) is widely used in oil spill detection due to its all-weather monitoring capability. Meanwhile, existing deep learning-based oil spill detection methods mainly rely on the classical U-Net framework and have achieved impressive results. However, SAR images exhibit high noise, blurry boundaries, and irregular shapes of target areas, as well as speckles and shadows, which lead to the loss of performance in existing algorithms. In this paper, we propose a novel network architecture to achieve more precise segmentation of oil spill areas by reintroducing rich semantic contextual information before obtaining the final segmentation mask. Specifically, the proposed architecture can re-fuse feature maps from different levels at the decoder end. We design a multi-convolutional layer (MCL) module to extract basic feature information from SAR images, and a feature extraction module (FEM) module further extracts and fuses feature maps generated by the U-Net decoder at different levels. Through these operations, the network can learn rich global and local contextual information, enable sufficient interaction of feature information at different stages, enhance the model’s contextual awareness, and improve its ability to recognize complex textures and blurry boundaries, thereby enhancing the segmentation accuracy of SAR images. Compared to many U-Net based segmentation networks, our method shows promising results and achieves state-of-the-art performance on multiple evaluation metrics.

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“…Oil film segmentation currently involves adaptive thresholding and machine learning classification methods. Oil spill radar image treatment is a tremendous question, apart from the above, and many scholars have conducted research on various aspects [17][18][19][20] The application of deep learning in marine radar oil spill monitoring technology is infrequent. We employed the YOLOv8 model here for marine radar oil spill region detection.…”

Section: Introductionmentioning

confidence: 99%

Marine Radar Oil Spill Detection Method Based on YOLOv8 and SA_PSO

Xu,

Huang,

Dong

et al. 2024

JMSE

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In the midst of a rapidly evolving economic landscape, the global demand for oil is steadily escalating. This increased demand has fueled marine extraction and maritime transportation of oil, resulting in a consequential and uneven surge in maritime oil spills. Characterized by their abrupt onset, rapid pollution dissemination, prolonged harm, and challenges in short-term containment, oil spill accidents pose significant economic and environmental threats. Consequently, it is imperative to adopt effective and reliable methods for timely detection of oil spills to minimize the damage inflicted by such incidents. Leveraging the YOLO deep learning network, this paper introduces a methodology for the automated detection of oil spill targets. The experimental data pre-processing incorporated denoise, grayscale modification, and contrast boost. Subsequently, realistic radar oil spill images were employed as extensive training samples in the YOLOv8 network model. The trained detection model demonstrated rapid and precise identification of valid oil spill regions. Ultimately, the oil films within the identified spill regions were extracted utilizing the simulated annealing particle swarm optimization (SA-PSO) algorithm. The proposed method for offshore oil spill survey presented here can offer immediate and valid data support for regular patrols and emergency reaction efforts.

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Oil Spill Detection in Synthetic Aperture Radar Images Using Lipschitz-Regularity and Multiscale Techniques

Cited by 17 publications

References 43 publications

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing

Marine Radar Oil Spill Detection Method Based on YOLOv8 and SA_PSO

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