Paper
26 October 2011 Neural networks for oil spill detection using TerraSAR-X data
Ruggero G. Avezzano, Domenico Velotto, Matteo Soccorsi, Fabio Del Frate, Susanne Lehner
Author Affiliations +
Proceedings Volume 8179, SAR Image Analysis, Modeling, and Techniques XI; 817911 (2011) https://doi.org/10.1117/12.898645
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic
Abstract
The increased amount of available Synthetic Aperture Radar (SAR) images involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In this paper we present the potentialities of TerraSAR-X (TS-X) data and Neural Network algorithms for oil spills detection. The radar on board satellite TS-X provides X-band images with a resolution of up to 1m. Such resolution can be very effective in the monitoring of coastal areas to prevent sea oil pollution. The network input is a vector containing the values of a set of features characterizing an oil spill candidate. The network output gives the probability for the candidate to be a real oil spill. Candidates with a probability less than 50% are classified as look-alikes. The overall classification performances have been evaluated on a data set of 50 TS-X images containing more than 150 examples of certified oil spills and well-known look-alikes (e.g. low wind areas, wind shadows, biogenic films). The preliminary classification results are satisfactory with an overall detection accuracy above 80%.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ruggero G. Avezzano, Domenico Velotto, Matteo Soccorsi, Fabio Del Frate, and Susanne Lehner "Neural networks for oil spill detection using TerraSAR-X data", Proc. SPIE 8179, SAR Image Analysis, Modeling, and Techniques XI, 817911 (26 October 2011); https://doi.org/10.1117/12.898645
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Cited by 6 scholarly publications.
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KEYWORDS
Neural networks

Synthetic aperture radar

Image segmentation

Image classification

Neurons

Radar

Satellites

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