Analysis on Adaptability of Monitoring Methods for Oil Spill by Using SAR Imagery

2014 ◽  
Vol 1030-1032 ◽  
pp. 1653-1656
Author(s):  
Qi Yao ◽  
Zhan Qiang Chang ◽  
Xiao Meng Liu ◽  
Li Yan Zhang ◽  
Chao Zhao
Keyword(s):  
Markov Chain ◽  
Oil Spill ◽  
Basic Principle ◽  
Ocean Wave ◽  
Ecological Environment ◽  
Monitoring Methods ◽  
Sar Images ◽  
Markov Chain Method ◽  
Otsu Method ◽  
Sar Imagery

As is well known, the ocean plays a key role in global ecological environment. In this paper, we introduced the basic principle of monitoring oil spill by using SAR images. On the basis of that, we systematically analyzed the applicability of various methods for monitoring oil spill by using SAR images. The conclusion shows that the ANN method and the OTSU method have the advantages of timeliness and efficiency in oil spill monitoring, while the Markov Chain method cost more time due to its capability in reducing the effect of internal ocean wave.

scholarly journals An improved Otsu method for oil spill detection from SAR images

Oceanologia ◽  
2017 ◽  
Vol 59 (3) ◽  
pp. 311-317 ◽  
Author(s):  
Fangjie Yu ◽  
Wuzi Sun ◽  
Jiaojiao Li ◽  
Yang Zhao ◽  
Yanmin Zhang ◽  
...  
Keyword(s):  
Oil Spill ◽  
Sar Images ◽  
Oil Spill Detection ◽  
Otsu Method

scholarly journals DARK SPOT DETECTION USING INTENSITY AND THE DEGREE OF POLARIZATION IN FULLY POLARIMETRIC SAR IMAGES FOR OIL POLUTION MONITORING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 749-753
Author(s):  
F. Zakeri ◽  
J. Amini
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Degree Of Polarization ◽  
Dark Spot ◽  
Sar Images ◽  
Simple Method ◽  
The Past ◽  
Spot Detection ◽  
Otsu Method ◽  
Image Pixels

Oil spill surveillance is of great environmental and economical interest, directly contributing to improve environmental protection. Monitoring of oil spills using synthetic aperture radar (SAR) has received a considerable attention over the past few years, notably because of SAR data abilities like all-weather and day-and-night capturing. The degree of polarization (DoP) is a less computationally complex quantity characterizing a partially polarized electromagnetic field. The key to the proposed approach is making use of DoP as polarimetric information besides intensity ones to improve dark patches detection as the first step of oil spill monitoring. In the proposed approach first simple intensity threshold segmentation like Otsu method is applied to the image. Pixels with intensities below the threshold are regarded as potential dark spot pixels while the others are potential background pixels. Second, the DoP of potential dark spot pixels is estimated. Pixels with DoP below a certain threshold are the real dark-spot pixels. Choosing the threshold is a critical and challenging step. In order to solve choosing the appropriate threshold, we introduce a novel but simple method based on DoP of potential dark spot pixels. Finally, an area threshold is used to eliminate any remaining false targets. The proposed approach is tested on L band NASA/JPL UAVSAR data, covering the Deepwater Horizon oil spill in the Gulf of Mexico. Comparing the obtained results from the new method with conventional approaches like Otsu, K-means and GrowCut shows better achievement of the proposed algorithm. For instance, mean square error (MSE) 65%, Overall Accuracy 20% and correlation 40% are improved.

scholarly journals LARGE OIL SPILL CLASSIFICATION USING SAR IMAGES BASED ON SPATIAL HISTOGRAM

2016 ◽  
Vol XLI-B8 ◽  
pp. 1183-1186 ◽  
Author(s):  
I. Schvartzman ◽  
S. Havivi ◽  
S. Maman ◽  
S. R. Rotman ◽  
D. G. Blumberg
Keyword(s):  
Oil Spill ◽  
Marine Pollution ◽  
Weather Conditions ◽  
Spatial Filter ◽  
Visual Interpretation ◽  
Sar Images ◽  
Detection Algorithms ◽  
Sar Imagery ◽  
Deep Horizon ◽  
Spatial Histogram

Among the different types of marine pollution, oil spill is a major threat to the sea ecosystems. Remote sensing is used in oil spill response. Synthetic Aperture Radar (SAR) is an active microwave sensor that operates under all weather conditions and provides information about the surface roughness and covers large areas at a high spatial resolution. SAR is widely used to identify and track pollutants in the sea, which may be due to a secondary effect of a large natural disaster or by a man-made one . The detection of oil spill in SAR imagery relies on the decrease of the backscattering from the sea surface, due to the increased viscosity, resulting in a dark formation that contrasts with the brightness of the surrounding area. <br><br> Most of the use of SAR images for oil spill detection is done by visual interpretation. Trained interpreters scan the image, and mark areas of low backscatter and where shape is a-symmetrical. It is very difficult to apply this method for a wide area. In contrast to visual interpretation, automatic detection algorithms were suggested and are mainly based on scanning dark formations, extracting features, and applying big data analysis. <br><br> We propose a new algorithm that applies a nonlinear spatial filter that detects dark formations and is not susceptible to noises, such as internal or speckle. The advantages of this algorithm are both in run time and the results retrieved. The algorithm was tested in genesimulations as well as on COSMO-SkyMed images, detecting the Deep Horizon oil spill in the Gulf of Mexico (occurred on 20/4/2010). The simulation results show that even in a noisy environment, oil spill is detected. Applying the algorithm to the Deep Horizon oil spill, the algorithm classified the oil spill better than focusing on dark formation algorithm. Furthermore, the results were validated by the National Oceanic and Atmospheric Administration (NOAA) data.

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 LARGE OIL SPILL CLASSIFICATION USING SAR IMAGES BASED ON SPATIAL HISTOGRAM

2016 ◽  
Vol XLI-B8 ◽  
pp. 1183-1186
Author(s):  
I. Schvartzman ◽  
S. Havivi ◽  
S. Maman ◽  
S. R. Rotman ◽  
D. G. Blumberg
Keyword(s):  
Oil Spill ◽  
Marine Pollution ◽  
Weather Conditions ◽  
Spatial Filter ◽  
Visual Interpretation ◽  
Sar Images ◽  
Detection Algorithms ◽  
Sar Imagery ◽  
Deep Horizon ◽  
Spatial Histogram

Among the different types of marine pollution, oil spill is a major threat to the sea ecosystems. Remote sensing is used in oil spill response. Synthetic Aperture Radar (SAR) is an active microwave sensor that operates under all weather conditions and provides information about the surface roughness and covers large areas at a high spatial resolution. SAR is widely used to identify and track pollutants in the sea, which may be due to a secondary effect of a large natural disaster or by a man-made one . The detection of oil spill in SAR imagery relies on the decrease of the backscattering from the sea surface, due to the increased viscosity, resulting in a dark formation that contrasts with the brightness of the surrounding area. <br><br> Most of the use of SAR images for oil spill detection is done by visual interpretation. Trained interpreters scan the image, and mark areas of low backscatter and where shape is a-symmetrical. It is very difficult to apply this method for a wide area. In contrast to visual interpretation, automatic detection algorithms were suggested and are mainly based on scanning dark formations, extracting features, and applying big data analysis. <br><br> We propose a new algorithm that applies a nonlinear spatial filter that detects dark formations and is not susceptible to noises, such as internal or speckle. The advantages of this algorithm are both in run time and the results retrieved. The algorithm was tested in genesimulations as well as on COSMO-SkyMed images, detecting the Deep Horizon oil spill in the Gulf of Mexico (occurred on 20/4/2010). The simulation results show that even in a noisy environment, oil spill is detected. Applying the algorithm to the Deep Horizon oil spill, the algorithm classified the oil spill better than focusing on dark formation algorithm. Furthermore, the results were validated by the National Oceanic and Atmospheric Administration (NOAA) data.

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 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.

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.

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