scholarly journals Oil Spill Map for Indian Sea Region based on Bhuvan- Geographic Information System using Satellite Images

2014 ◽  
Vol XL-8 ◽  
pp. 1085-1090
Author(s):  
L. J. Vijaya kumar ◽  
J. K. Kishore ◽  
P. Kesava Rao ◽  
M. Annadurai ◽  
C. B. S. Dutt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Oil Spills ◽  
Geographic Information ◽  
Gas Production ◽  
Coast Guard ◽  
Sar Images ◽  
Distribution Maps ◽  
Oil Spill Detection ◽  
Sar Imagery

Oil spills in the ocean are a serious marine disaster that needs regular monitoring for environmental risk assessment and mitigation. Recent use of Polarimetric SAR imagery in near real time oil spill detection systems is associated with attempts towards automatic and unambiguous oil spill detection based on decomposition methods. Such systems integrate remote sensing technology, geo information, communication system, hardware and software systems to provide key information for analysis and decision making. <br><br> Geographic information systems (GIS) like BHUVAN can significantly contribute to oil spill management based on Synthetic Aperture Radar (SAR) images. India has long coast line from Gujarat to Bengal and hundreds of ports. The increase in shipping also increases the risk of oil spills in our maritime zone. The availability of RISAT-1 SAR images enhances the scope to monitor oil spills and develop GIS on Bhuvan which can be accessed by all the users, such as ships, coast guard, environmentalists etc., The GIS enables realization of oil spill maps based on integration of the geographical, remote sensing, oil & gas production/infrastructure data and slick signatures detected by SAR. SAR and GIS technologies can significantly improve the realization of oil spill footprint distribution maps. Preliminary assessment shows that the Bhuvan promises to be an ideal solution to understand spatial, temporal occurrence of oil spills in the marine atlas of India. The oil spill maps on Bhuvan based GIS facility will help the ONGC and Coast Guard organization.

Sea Oil Spill Detection Method Using SAR Imagery Combined with Object-Based Image Analysis and Fuzzy Logic

2014 ◽  
Vol 1065-1069 ◽  
pp. 3192-3200 ◽  
Author(s):  
Teng Fei Su ◽  
Hong Yu Li ◽  
Ting Xi Liu
Keyword(s):  
Fuzzy Logic ◽  
Oil Spill ◽  
Oil Spills ◽  
Promising Result ◽  
Sar Images ◽  
Oil Spill Detection ◽  
Envisat Asar ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sar Imagery

Synthetic aperture radar (SAR), a sensor with all weather and day and night working capacity, has been considered one of the most powerful tools for sea surface oil spill detection. However, lookalikes frequently appear in SAR images, limiting the operational use of SAR to detect oil spilled at sea. 20 scenes of Envisat ASAR images, which were acquired during the oil spill accident in the Gulf of Mexico in 2010, are utilized, with the objective to study how to better differentiate oil spills from lookalikes. 145 and 134 samples for oil spill and lookalike, respectively, are extracted, and their object-based geometric, physical and textural features are analyzed, in order to find the most effective features for oil spill classification. Based on the results of feature analysis, fuzzy logic (FL) is employed to construct a classifier for oil spill detection. One advantage of the proposed method is that it can produce the crisp probability of a dark segment being oil spill. The experiment shows that our method can derive promising result.

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 Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning

2020 ◽  
Vol 12 (14) ◽  
pp. 2260 ◽  
Author(s):  
Filippo Maria Bianchi ◽  
Martine M. Espeseth ◽  
Njål Borch
Keyword(s):  
Deep Learning ◽  
Oil Spill ◽  
Large Scale ◽  
Oil Spills ◽  
Sar Images ◽  
Oil Spill Detection ◽  
Learning Framework ◽  
Large Scale Data ◽  
Human Operators ◽  
Scale Detection

We propose a deep-learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. Through a carefully designed neural network model for image segmentation trained on an extensive dataset, we obtain state-of-the-art performance in oil spill detection, achieving results that are comparable to results produced by human operators. We also introduce a classification task, which is novel in the context of oil spill detection in SAR. Specifically, after being detected, each oil spill is also classified according to different categories of its shape and texture characteristics. The classification results provide valuable insights for improving the design of services for oil spill monitoring by world-leading providers. Finally, we present our operational pipeline and a visualization tool for large-scale data, which allows detection and analysis of the historical occurrence of oil spills worldwide.

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 Detection of Oil Spill Using SAR Imagery Based on AlexNet Model

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xinzhe Wang ◽  
Jiaxu Liu ◽  
Shuai Zhang ◽  
Qiwen Deng ◽  
Zhuo Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Oil Spill ◽  
Oil Spills ◽  
Speckle Noise ◽  
Optimization Methods ◽  
Sar Images ◽  
Connection Weight ◽  
Imaging Characteristics ◽  
Sar Imagery

Synthetic aperture radar (SAR) plays an irreplaceable role in the monitoring of marine oil spills. However, due to the limitation of its imaging characteristics, it is difficult to use traditional image processing methods to effectively extract oil spill information from SAR images with coherent speckle noise. In this paper, the convolutional neural network AlexNet model is used to extract the oil spill information from SAR images by taking advantage of its features of local connection, weight sharing, and learning for image representation. The existing remote sensing images of the oil spills in recent years in China are used to build a dataset. These images are enhanced by translation and flip of the dataset, and so on and then sent to the established deep convolutional neural network for training. The prediction model is obtained through optimization methods such as Adam. During the prediction, the predicted image is cut into several blocks, and the error information is removed by corrosion expansion and Gaussian filtering after the image is spliced again. Experiments based on actual oil spill SAR datasets demonstrate the effectiveness of the modified AlexNet model compared with other approaches.

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 Reconstruction Of Oil Spill Trajectory In The Java Sea, Indonesia Using Sar Imagery

2021 ◽  
Vol 14 (1) ◽  
pp. 177-184
Author(s):  
Amarif Abimanyu ◽  
Widodo S. Pranowo ◽  
Ibnu Faizal ◽  
Najma K. A. Afandi ◽  
Noir P. Purba
Keyword(s):  
Oil Spill ◽  
Satellite Images ◽  
Oil Spills ◽  
Surface Wind ◽  
Ocean Current ◽  
Sar Images ◽  
Current Pattern ◽  
Java Sea ◽  
Sar Imagery ◽  
Two Parameters

Oil spill phenomena in the ocean possess a very serious threat to ocean health. On the ocean surface, oil slicks immediately start to spread and mostly end up in the ecosystem. Furthermore, it could threaten the organisms living in the ocean or impact nearby coastal area. The aim of this research was to investigate the trajectories of oil spill based on a real accident in the Java Sea. Tracking oil spills using satellite images is an efficient method that provides valuable information about trajectories, locations and the spread intensity. The objective of this study was to periodically track the trajectory of the oil spill from the Karawang incident using Sentinel-1 Synthetic Aperture Radar (SAR) images. Pre-processing of the images consisted of radiometric and geometric corrections. After the corrections, SAR images were mapped and plotted accordingly. To understand the oil spill trajectories in relation to the oceanic processes, the ocean current pattern map and surface wind roses were also analysed. The processed images from July to October 2019 show a trajectory dominated by the oil spill layers movement towards the west to northwest from the original location along with a decrease in the detected oil spill area over time. The identified trajectories of the oil spill followed the ocean current pattern and surface winds. Thus, these two parameters were considered to be the main factors responsible for the oil spill drift.

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