scholarly journals Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

2019 ◽  
Vol 11 (7) ◽  
pp. 756 ◽  
Author(s):  
Peng Liu ◽  
Ying Li ◽  
Bingxin Liu ◽  
Peng Chen ◽  
and Jin Xu
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection Method ◽  
Adaptive Thresholding ◽  
Oil Spill Detection ◽  
Radar Images ◽  
X Band ◽  
Marine Radar

Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.

Oil spill detection method using X-band marine radar imagery

2015 ◽  
Vol 9 (1) ◽  
pp. 095985 ◽  
Author(s):  
Xueyuan Zhu ◽  
Ying Li ◽  
Haiyang Feng ◽  
Bingxin Liu ◽  
Jin Xu
Keyword(s):  
Oil Spill ◽  
Detection Method ◽  
Oil Spill Detection ◽  
X Band ◽  
Radar Imagery ◽  
Marine Radar

Oil spill extraction by X-band marine radar using texture analysis and adaptive thresholding

2019 ◽  
Vol 10 (6) ◽  
pp. 583-589 ◽  
Author(s):  
Peng Liu ◽  
Ying Li ◽  
Jin Xu ◽  
Tong Wang
Keyword(s):  
Texture Analysis ◽  
Oil Spill ◽  
Adaptive Thresholding ◽  
X Band ◽  
Marine Radar

scholarly journals Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

2020 ◽  
Vol 12 (20) ◽  
pp. 3416
Author(s):  
Shamsudeen Temitope Yekeen ◽  
Abdul-Lateef Balogun
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Oil Spill ◽  
Oil Spills ◽  
Biogenic Elements ◽  
Trajectory Prediction ◽  
Oil Spill Detection ◽  
Remote Sensing Technology ◽  
Sensing Technology

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

Oil spill extraction from X-band marine radar images by power fitting of radar echoes

2021 ◽  
Vol 12 (4) ◽  
pp. 345-352
Author(s):  
Peng Liu ◽  
Yancheng Zhao ◽  
Bingxin Liu ◽  
Ying Li ◽  
Peng Chen
Keyword(s):  
Oil Spill ◽  
Radar Images ◽  
Radar Echoes ◽  
X Band ◽  
Marine Radar

scholarly journals Oil Spill Detection Using Satellite Imagery

2021 ◽  
Vol 2 (4) ◽  
pp. 1-1
Author(s):  
Amber Bonnington ◽  
◽  
Meisam Amani ◽  
Hamid Ebrahimy ◽  
◽  
...  
Keyword(s):  
Change Detection ◽  
Oil Spill ◽  
Deep Water ◽  
Oil Spills ◽  
Detection Method ◽  
Oil Spill Detection ◽  
New Development ◽  
Before And After ◽  
The Difference ◽  
Remote Sensing Techniques

<span>Since oil exploration began, oil spills have become a serious problem. When drilling for oil, there is always a risk of an oil spill. With the new development of technology over the years, oil spill detection has become much easier making the clean-up of a spill to happen much faster reducing the risk of a large spread. In this study, remote sensing techniques were used to detect the Deep-water Horizon oil spill through a change detection method. The change detection method allows the viewer to determine the difference of an area before and after an oil spill as well as detect the irregular difference on a surface. To confirm the effectiveness of change detection method, two approaches were used each showing the differences in the images before and after the spill allowing the size and shape to be identified. The swipe tool in the ArcGIS software was used to visually show the changes. The difference tool was also used to both visually and statistically to investigate the difference before and after the Deep-water Horizon oil spill event.</span>

scholarly journals APPLICATION OF ACTIVE REMOTE SENSING METHODS FOR OIL SPILL DETECTION

2021 ◽  
Vol 6 ◽  
pp. 213-218
Author(s):  
Pavel I. Nazdrachev ◽  
Alexander Yu. Chermoshentsev
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Oil Spills ◽  
Oil Spill Detection ◽  
Radar Images ◽  
Remote Sensing Methods

The article describes the implementation of the method for processing radar images from the Sentinel-1 satellite on the territory of the Sakhalin Region, the purpose of which is to detect oil spills. The possibility of using this technique for the prompt detection of oil spills in water areas, as well as for monitoring is shown.

scholarly journals OILSPILL AND LOOK-ALIKE SPOTS FROM SAR IMAGERY USING OTSU METHOD AND ARTIFICIAL NEURAL NETWORK

2020 ◽  
Vol 4 (11) ◽  
pp. 1-10
Author(s):  
M. Sornam
Keyword(s):  
Image Segmentation ◽  
Oil Spill ◽  
Oil Spills ◽  
Marine Ecosystem ◽  
Dark Spot ◽  
Oil Spill Detection ◽  
Otsu Method ◽  
Artificial Neural ◽  
Propagation Network

Oil spill pollution plays a significant role in damaging marine ecosystem. Discharge of oil due to tanker accidents has the most dangerous effects on marine environment. The main waste source is the ship based operational discharges. Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. One major advantage of SAR is that it can generate imagery under all weather conditions. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish ‘oil spills’ from ‘look-alikes’. The features of detected dark spot are then extracted and classified to discriminate oil spills from look-alikes. This paper describes the development of a new approach to SAR oil spill detection using Segmentation method and Artificial Neural Networks (ANN). A SAR-based oil-spill detection process consists of three stages: image segmentation, feature extraction and object recognition (classification) of the segmented objects as oil spills or look-alikes. The image segmentation was performed with Otsu method. Classification has been done using Back Propagation Network and this network classifies objects into oil spills or look-alikes according to their feature parameters. Improved results have been achieved for the discrimination of oil spills and look-alikes.

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