Journal
APPLIED SOFT COMPUTING
Volume 84, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2019.105716
Keywords
SAR; Remote sensing; Oil spill; Image segmentation; Deep learning
Categories
Funding
- Xunta de Galicia, Spain [ED431B 2018/42, ED431G/01]
- European Union (European Regional Development Fund-ERDF)
- Spanish Ministry of Science, Innovation and Universities [RTI2018-095076-B-C22, ESP2016-80079-C2-2-R]
- Ministry of Economy, Industry and Competitiveness, Spain [RTI2018-095076-B-C22, ESP2016-80079-C2-2-R]
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Synthetic aperture radar (SAR) images are a valuable source of information for the detection of marine oil spills. For their effective analysis, it is important to have segmentation algorithms that can delimit possible oil spill areas. This article addresses the application of clustering, logistic regression and convolutional neural network algorithms for the detection of oil spills in Envisat and Sentinel-1 satellite images. Large oil spills do not occur frequently so that the identification of a pixel as oil is relatively uncommon. Metrics based on Precision-Recall curves have been employed because they are useful for problems with an imbalance in the number of samples from the classes. Although logistic regression and clustering algorithms can be considered useful for oil spill segmentation, the combination of convolutional techniques and neural networks achieves the best results with low computing time. A convolutional neural network has been integrated into a decision support system in order to facilitate decision-making and data analysis of possible oil spill events. (C) 2019 The Authors. Published by Elsevier B.V.
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