OIL SPILL DETECTION WITH REMOTE SENSORS

2016 ◽  
Author(s):  
Kristina Pilžis ◽  
Vaidotas Vaišis
Keyword(s):  
Remote Sensing ◽  
Surface Properties ◽  
Oil Spill ◽  
Marine Environment ◽  
Oil Spills ◽  
Sea Surface ◽  
Optical Remote Sensing ◽  
Oil Spill Detection ◽  
Remote Sensors ◽  
Microwave Sensors

Accurate detection and forecasting of oil spills and their trajectories is beneficial for monitoring and conservation of the marine environment. The most common techniques of oil spill tracking are remote sensing from an aircraft and satellites. Remote sensors work by detecting sea surface properties and the most effective of them are laser fluorosensors, optical remote sensing (visible, infrared, ultraviolet) and microwave sensors. Possibilities and advantages of their use are reviewed in this article.

scholarly journals Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

2021 ◽  
Vol 14 (1) ◽  
pp. 157
Author(s):  
Zongchen Jiang ◽  
Jie Zhang ◽  
Yi Ma ◽  
Xingpeng Mao
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Oil Spills ◽  
Heavy Oils ◽  
Marine Oil ◽  
Oil Spill Detection ◽  
Moment Estimation ◽  
Marine Oil Spills ◽  
Oil Films

Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight.

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.

scholarly journals Leveraging Satellite Sensors for Oil Spill Detection

Eos ◽  
2020 ◽  
Vol 101 ◽  
Author(s):  
Sarah Derouin
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Oil Spill Detection ◽  
Remote Sensors ◽  
Satellite Sensors

By using multiple remote sensors, scientists can quickly estimate the nature and thickness of oil spills—important factors for containment efforts.

scholarly journals SEA OIL SPILL DETECTION USING SELF-SIMILARITY PARAMETER OF POLARIMETRIC SAR DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1661-1666 ◽  
Author(s):  
S. Tong ◽  
Q. Chen ◽  
X. Liu
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Marine Ecosystem ◽  
Sea Surface ◽  
Synthetic Aperture ◽  
Scattering Mechanism ◽  
Self Similarity ◽  
Similarity Parameter ◽  
Polarimetric Synthetic Aperture Radar ◽  
Oil Spill Detection

The ocean oil spills cause serious damage to the marine ecosystem. Polarimetric Synthetic Aperture Radar (SAR) is an important mean for oil spill detections on sea surface. The major challenge is how to distinguish oil slicks from look-alikes effectively. In this paper, a new parameter called self-similarity parameter, which is sensitive to the scattering mechanism of oil slicks, is introduced to identify oil slicks and reduce false alarm caused by look-alikes. Self-similarity parameter is small in oil slicks region and it is large in sea region or look-alikes region. So, this parameter can be used to detect oil slicks from look-alikes and water. In addition, evaluations and comparisons were conducted with one Radarsat-2 image and two SIR-C images. The experimental results demonstrate the effectiveness of the self-similarity parameter for oil spill detection.

Oil Spill Detection via Multitemporal Optical Remote Sensing Images: A Change Detection Perspective

2017 ◽  
Vol 14 (3) ◽  
pp. 324-328 ◽  
Author(s):  
Sicong Liu ◽  
Mingmin Chi ◽  
Yangxiu Zou ◽  
Alim Samat ◽  
Jon Atli Benediktsson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Oil Spill ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Oil Spill Detection

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 Optical Remote Sensing of Oil Spills in the Ocean: What Is Really Possible?

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chuanmin Hu ◽  
Yingcheng Lu ◽  
Shaojie Sun ◽  
Yongxue Liu
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Current Literature ◽  
Oil Spills ◽  
Numerical Models ◽  
Free Water ◽  
Optical Remote Sensing ◽  
Laboratory Measurements ◽  
Remote Sensing Imagery ◽  
Sun Light

Optical remote sensing (ORS) of reflected sun light has been used to assess oil spills in the ocean for several decades. While most applications are toward simple presence/absence detections based on the spatial contrast between oiled water and oil-free water, recent advances indicate the possibility of classifying oil types and quantifying oil volumes based on their spectral contrasts with oil-free water. However, a review of the current literature suggests that there is still confusion on whether this is possible and, if so, how. Here, based on the recent findings from numerical models, laboratory measurements, and applications to satellite or airborne imagery, we attempt to clarify this situation by summarizing (1) the optics behind oil spill remote sensing, and in turn, (2) how to interpret optical remote sensing imagery based on optical principles. In the end, we discuss the existing limitations and challenges as well as pathways forward to advance ORS of oil spills.

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