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Oil Spill Identification based on Textural Information of SAR Image
2008
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium
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Experiments on several SAR images show that method proposed in this paper can improve the detection and identification of oil spill in SAR images. ...
is used to identify oil spills in SAR images. ...
Because of the complexity of marine system and the relatively little information contained in SAR image, the automatic algorithm of oil spill identification in SAR image has not been well developed till ...
doi:10.1109/igarss.2008.4779971
dblp:conf/igarss/Zhang0TW08
fatcat:4iqzjhlqdbffnb52lif7nj5u3y
Oil spill detection from SAR image using SVM based classification
2013
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In this paper, the potential of fully polarimetric L-band SAR data for detecting sea oil spills is investigated using polarimetric decompositions and texture analysis based on SVM classifier. ...
Results show that the Krogager polarimetric decomposition method has the satisfying result for detection of sea oil spill on the sea surface and the texture analysis presents the good results. ...
This characteristic leads to identification of the spills on SAR images. The advent of the full-polarimetric satellite SAR systems (i.e. ...
doi:10.5194/isprsarchives-xl-1-w3-55-2013
fatcat:ovvdjrjezra2bp7mpn36sqmb5e
Oil Spill Identification and Monitoring from Sentinel-1 Sar Satellite Earth Observations: a Machine Learning Approach
2021
Chemical Engineering Transactions
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One of the main aspects of oil spreading over sea surface is that it dampens the capillary waves and so, the backscatter radio waves are suppressed. ...
Identification of an oil spill is essential to evaluate the potential spread and float from the source to coastal terrains, and their continued monitoring is essential for managing the environmental protection ...
Figure 2 : 2 Figure 2: Oil spill identification based on SAR images on October 8 th , 2018, 07:28 (a) and 19:21 (b)
Table 1 : 1 List of relevant features extracted from SAR images Feature Category Feature ...
doi:10.3303/cet2186064
doaj:7aa19b49136d4f129d0dfae89daec7ab
fatcat:pichfkmvxbc45jx4l7ledipisq
Mapping Oil Spills from Dual-Polarized SAR Images Using an Artificial Neural Network: Application to Oil Spill in the Kerch Strait in November 2007
2018
Sensors
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Further improved oil spill detection algorithms, which are based on the intensity difference between the target and background pixels, have been proposed by exploiting (i) the texture information of sea ...
For this, we focus on (i) the oil spill enhancement from the intensity and phase information of the co-polarized TerraSAR-X imagery and (ii) the precise identification of oil-covered surface from the oil ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s18072237
pmid:29997367
pmcid:PMC6069476
fatcat:erlc5oit25ck7nvvyodp25zfka
Detection of oil spills based on gray level co-occurrence matrix and support vector machine
2022
Frontiers in Environmental Science
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Aiming at the identification accuracy of small oil spill accident in offshore port area and the problem of day and night reconnaissance, this study takes thermal infrared remote sensing images of oil leakage ...
captured by UAV as the research object and proposes an oil spill detection method based on a Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) method. ...
proposed an ocean oil spill detection method based on multi-feature polarized SAR data using the random forest method to classify image data. ...
doi:10.3389/fenvs.2022.1049880
fatcat:fdbv6r4ffrhnpmsf727nhfkdq4
Combining Features to Improve Oil Spill Classification in SAR Images
[chapter]
2006
Lecture Notes in Computer Science
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In this paper three feature sets are used to identify the oil slicks in SAR images. ...
This analysis is very suitable for remote sensing of environment applications concerning marine oil pollution. ...
The overall performance of the classifier is evaluated for different feature sets based on geometrics and texture attributes aiming at optimizing oil spills detection in SAR images. ...
doi:10.1007/11815921_103
fatcat:j4vayyk3lvgw5e33pky7qxt6hq
Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review
2020
Remote Sensing
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Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions ...
The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. ...
Most textural features utilized in oil spill detection are based on GLCM. ...
doi:10.3390/rs12203338
fatcat:awufdmqg4bhgpi2cmsxy5b52pa
Modification of fractal algorithm for oil spill detection from RADARSAT-1 SAR data
2009
International Journal of Applied Earth Observation and Geoinformation
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G Model JAG-229; No of Pages 7 Please cite this article in press as: Marghany, M., et al., Modification of fractal algorithm for oil spill detection from RADARSAT-1 SAR data. Int. ...
Oil spill, look-alikes and sea surface roughness are discriminated well based on the ROC curve. ...
The fractal dimension maps show a good discrimination between different textures on the RADARSAT-1 SAR images and correlate well with image texture regions. ...
doi:10.1016/j.jag.2008.09.002
fatcat:ypw5wmp6vfa2jkave6gxsghwle
Detection of Oil Spill Using SAR Imagery Based on AlexNet Model
2021
Computational Intelligence and Neuroscience
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Experiments based on actual oil spill SAR datasets demonstrate the effectiveness of the modified AlexNet model compared with other approaches. ...
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 ...
In SAR images, oil spill can be identified from the perspective of features such as geometry, grayscale, and texture [19] . ...
doi:10.1155/2021/4812979
fatcat:rqyeoo4xyndc5mgzu4kfojvrte
Oil Spill Identification from Satellite Images Using Deep Neural Networks
2019
Remote Sensing
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Oil spill is considered one of the main threats to marine and coastal environments. ...
Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing. ...
On the other hand, oil spills and look-alikes are usually extended in smaller regions of SAR images. ...
doi:10.3390/rs11151762
fatcat:m46q4gegenecvondasrpod6dbm
TESTING THE GENERALIZATION EFFICIENCY OF OIL SLICK CLASSIFICATION ALGORITHM USING MULTIPLE SAR DATA FOR DEEPWATER HORIZON OIL SPILL
2012
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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ACKNOWLEDGEMENTS The authors would like to thank MDA and ASF for providing SAR data. ...
METHODOLOGY Radar images have an advantage for oil spill detection due to the dampening effect of oil on capillary waves causing them to be detectable as black patches on images. ...
In terms of these features, some classification algorithms based on statistical, neural, fuzzy, rule based, boosting algorithms etc. are used for identification of the dark areas in a manner of binary ...
doi:10.5194/isprsarchives-xxxix-b7-67-2012
fatcat:ddanmn4avbdlbb45zg5vxruzoy
A deep neural network for oil spill semantic segmentation in SAR images
2018
Zenodo
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The deployed CNN was trained using multiple SAR images acquired from the sentinel-1 satellite provided by ESA and based on EMSA records for maritime pollution events. ...
This paper describes the development of an approach that combines the merits of a deep CNN with SAR imagery in order to provide a fully automated oil spill detection system. ...
The annotation of the images was based on information provided by EMSA and human identification (manually annotation). ...
doi:10.5281/zenodo.3497132
fatcat:oxnuf2o5gndorhxcz4jpxz4wd4
A System for Automatic Identification of Oil Spill in ENVISAT ASAR Images
2008
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium
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An automatic oil spill identification system was introduced in this paper. It was designed to work independently to detect oil spills in ENVISAT ASAR level 1b images (of WS, IMP and APP mode). ...
The system works well for its accuracy of 85% in discriminating between oil spills and look-alikes in the case of 60 scenes of examined images. ...
An effective monitoring and identification of polluted oil spill on the sea surface is operationally required. ...
doi:10.1109/igarss.2008.4779621
dblp:conf/igarss/Tian0W08
fatcat:ylhtd57p7bhylnr35jd37z5l2u
Large-scale detection and categorization of oil spills from SAR images with deep learning
[article]
2020
arXiv
pre-print
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By means of a carefully designed neural network model for image segmentation trained on an extensive dataset, we are able to obtain state-of-the-art performance in oil spill detection, achieving results ...
We propose a deep learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. ...
We acknowledge the work of the following researchers at NORCE: Ingar Artnzen for processing and preparing the SAR dataset, Per Egil Kummervold for contributing to the design of deep learning methods, and ...
arXiv:2006.13575v1
fatcat:lld3zfftnrhwhcjnqvwzehtjtu
Automatic identification of oil spills on satellite images
2006
Environmental Modelling & Software
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A fully automated system for the identification of possible oil spills present on Synthetic Aperture Radar (SAR) satellite images based on artificial intelligence fuzzy logic has been developed. ...
The system analyzes the satellite images and assigns the probability of a dark image shape to be an oil spill. ...
on an SAR image to be an oil spill is a function of many factors. ...
doi:10.1016/j.envsoft.2004.11.010
fatcat:vz3ockuk2fctxoza2uakxvihle
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