Ocean oil spill detection from SAR images based on multi-channel deep learning semantic segmentation

Hasimoto-Beltrán, Rogelio; Canul-Ku, Mario; Mendez, G. M. Diaz; Ocampo‐Torres, Francisco J.; Esquivel–Trava, Bernardo

doi:10.1016/j.marpolbul.2023.114651

Cited by 22 publications

(5 citation statements)

References 30 publications

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“…Ships create waves with distinct roughness patterns, while oil spills typically dampen waves and thus create a smoother surface. Such patterns can be robustly detected using simple algorithms [39][40][41][42]; therefore, they are widely used in practice. In addition, SAR signals are sensitive to 3D structure or height changes for ground objects due to the geometry mechanism of side-view radar waves and the shadowing and layover effects of radar, which benefit the detection of 3D structural changes of the object.…”

Section: D Active Data: Synthetic Aperture Radar Datamentioning

confidence: 99%

“…Oil spills detection: Oil spills pose severe environmental issues and require rapid detection to localize the impacted regions. The work of [41,42,192] focus on the use of ENVISAT advanced synthetic aperture radar (ASAR) imagery for object detection vessels and oil slick. The case studies include images captured during notable ecological disasters, such as the Deepwater Horizon blowout in the Gulf of Mexico, the Hebei Spirit oil tanker collision off South Korea's west coast, and incidents over the Black Sea, where the employed studies show effectiveness in using remote sensing methods for oil spill detection and tracking.…”

Section: Environmental-ecological Monitoring and Managementmentioning

confidence: 99%

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Remote Sensing Object Detection in the Deep Learning Era—A Review

Gui,

Song,

Qin

et al. 2024

Remote Sensing

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Given the large volume of remote sensing images collected daily, automatic object detection and segmentation have been a consistent need in Earth observation (EO). However, objects of interest vary in shape, size, appearance, and reflecting properties. This is not only reflected by the fact that these objects exhibit differences due to their geographical diversity but also by the fact that these objects appear differently in images collected from different sensors (optical and radar) and platforms (satellite, aerial, and unmanned aerial vehicles (UAV)). Although there exists a plethora of object detection methods in the area of remote sensing, given the very fast development of prevalent deep learning methods, there is still a lack of recent updates for object detection methods. In this paper, we aim to provide an update that informs researchers about the recent development of object detection methods and their close sibling in the deep learning era, instance segmentation. The integration of these methods will cover approaches to data at different scales and modalities, such as optical, synthetic aperture radar (SAR) images, and digital surface models (DSM). Specific emphasis will be placed on approaches addressing data and label limitations in this deep learning era. Further, we survey examples of remote sensing applications that benefited from automatic object detection and discuss future trends of the automatic object detection in EO.

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Section: D Active Data: Synthetic Aperture Radar Datamentioning

confidence: 99%

Section: Environmental-ecological Monitoring and Managementmentioning

confidence: 99%

Remote Sensing Object Detection in the Deep Learning Era—A Review

Gui,

Song,

Qin

et al. 2024

Remote Sensing

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“…Nunziata et al also utilized pedestal height of the polarization signature to distinguish oil spills from the sea background [ 36 ]. More recently, researchers have also begun integrating advanced artificial intelligence techniques such as convolutional neural networks by Ronci et al [ 37 ], deep convolutional neural networks by Zeng and Wang [ 38 ], and multichannel deep neural networks by Hasimoto-Beltran et al [ 39 ].…”

Section: The Evidentiary Potential Of Satellite Imagery In Environmen...mentioning

confidence: 99%

The evidentiary challenges of using satellite technologies to enforce ship-source marine pollution standards on the high seas

Zhang,

Yang

2024

Heliyon

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“…For each original input SAR image, the MCNN algorithm requires resizing the corresponding dimensions to 299 pixels for both height and width. Nevertheless, in the direct implementation of oceanic phenomenon classification, there is a potential for the loss of certain meaningful features and the attenuation of information that is considered less pertinent [57]. Hence, the an image edge filter is implemented to retain the critical structural attributes inherent in SAR imagettes.…”

Section: Multi-feature Fusionmentioning

confidence: 99%

A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification

Bai,

Zhang,

Wang

et al. 2024

Applied Sciences

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High-resolution synthetic aperture radars (SARs) are becoming an indispensable environmental monitoring system to capture the important geophysical phenomena on the earth and sea surface. However, there is a lack of comprehensive models that can orchestrate such large-scale datasets from numerous satellite missions such as GaoFen-3 and Sentinel-1. In addition, these SAR images of different ocean scenes need to convey a variety of high-level classification features in oceanic and atmospheric phenomena. In this study, we propose a multichannel neural network (MCNN) that supports oceanic SAR scene classification for limited oceanic data samples according to multi-feature fusion, data augmentation, and multichannel feature extraction. To exploit the multichannel semantics of SAR scenes, the multi-feature fusion module effectively combines and reshapes the spatiotemporal SAR images to preserve their structural properties. This fine-grained feature augmentation policy is extended to improve the data quality so that the classification model is less vulnerable to both small- and large-scale data. The multichannel feature extraction also aggregates different oceanic features convolutionally extracted from ocean SAR scenes to improve the classification accuracy of oceanic phenomena with different scales. Through extensive experimental analysis, our MCNN framework has demonstrated a commendable classification performance, achieving an average precision rate of 96%, an average recall rate of 95%, and an average F-score of 95% across ten distinct oceanic phenomena. Notably, it surpasses two state-of-the-art classification techniques, namely, AlexNet and CMwv, by margins of 23.7% and 18.3%, respectively.

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Ocean oil spill detection from SAR images based on multi-channel deep learning semantic segmentation

Cited by 22 publications

References 30 publications

Remote Sensing Object Detection in the Deep Learning Era—A Review

Remote Sensing Object Detection in the Deep Learning Era—A Review

The evidentiary challenges of using satellite technologies to enforce ship-source marine pollution standards on the high seas

A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification

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