Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network
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TLDR
Experimental results have proven that the proposed approach is effective and applicable to classify the ocean oil spill and the convergence ability of optimized WNN can be enhanced, improving overall classification accuracy of oceanOil spill.Abstract:
Oil spill accidents from ship or oil platform cause damage to marine and coastal environment and ecosystems. To monitor such spill events from space, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been greatly used in improving oil spill observation. Aiming to promote ocean oil spill classification accuracy, we developed a new oil spill identification method by combining multiple fully polarimetric SAR features data with an optimized wavelet neural network classifier (WNN). Two sets of RADARSAT-2 fully polarimetric SAR data are applied to test the validity of the developed method. The experimental results show that: (1) the convergence ability of optimized WNN can be enhanced, improving overall classification accuracy of ocean oil spill, in comparison to the classification results based on a common un-optimized WNN classifier; and (2) the joint use of the multiple fully Pol-SAR features as the inputs of the classifier can achieve better classification result than that only with single fully Pol-SAR feature. The developed method can improve classification accuracy by 4.96% and 7.75%, compared with the classification results with un-optimized WNN and only with one single fully polarimetric SAR feature. The classification overall accuracy based on the proposed approach can reach 97.67%. Experimental results have proven that the proposed approach is effective and applicable to classify the ocean oil spill.read more
Citations
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Journal ArticleDOI
Oil Spill Identification from Satellite Images Using Deep Neural Networks
Marios Krestenitis,Georgios Orfanidis,Konstantinos Ioannidis,Konstantinos Avgerinakis,Stefanos Vrochidis,Ioannis Kompatsiaris +5 more
TL;DR: Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors, including DeepLabv3+, which presented the best performance.
Journal ArticleDOI
Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review
Rami Al-Ruzouq,Mohamed Barakat A. Gibril,Abdallah Shanableh,Abubakir Kais,Osman Hamed,Saeed Al-Mansoori,Mohamad Ali Khalil +6 more
TL;DR: Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study.
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Machine Learning‐Enabled Smart Sensor Systems
TL;DR: A review of the recent sensing applications, which harness ML enabled smart sensor systems, and how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits are presented.
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Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter
TL;DR: A novel oil spill detection method based on multiple features of polarimetric SAR data is proposed to improve the detection accuracy and the self-similarity parameter, which is sensitive to the randomness of the scattering target, is introduced to enhance the discrimination ability between oil slicks and look-alikes.
Journal ArticleDOI
A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery
TL;DR: A novel oil spill identification method based on multi-layer deep feature extraction by CNN that can improve the accuracy of oil spill detection, reduce the false alarm rate, and effectively distinguish an oil spill from a biogenic slick is proposed.
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