Multi-frequency and multi-polarization analysis of oil slicks using TerraSAR-X and RADARSAT-2 data
Singha S, Ressel R, Lehner S (2016)
Publication Type: Conference contribution
Publication year: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2016-November
Pages Range: 4035-4038
Conference Proceedings Title: International Geoscience and Remote Sensing Symposium (IGARSS)
Event location: Beijing, CHN
ISBN: 9781509033324
DOI: 10.1109/IGARSS.2016.7730049
Abstract
The use of fully polarimetric SAR data for oil spill detection is relatively new and shows great potential for operational off-shore platform monitoring. Greater availability of these kind of SAR data calls for a development of time critical processing chain capable of detecting and distinguishing oil spills from 'look-alikes'. This paper describes the development of an automated Near Real Time (NRT) oil spill detection processing chain based on quad-pol RADARSAT-2 (RS-2) and quad-pol TerraSAR-X (TS-X) images, wherein we use polarimetric features (e.g. Lexicographic and Pauli Based features) to characterize oil spills and look-alikes. Numbers of TS-X and RS-2 images have been acquired over known off-shore platforms along with some near coincident (spatially and temporally) acquisition. Ten polarimetric feature parameters were extracted from different types of oil (e.g. crude oil, emulsion etc) and 'look-alike' (e.g. plant oil, met-oceanic phenomenon etc) spots and divided into training and validation dataset seperately for TerraSAR-X RADARSAT-2. Extracted features were then used for training and validation of a pixel based Artificial Neural Network (ANN) classifier. Initial performance estimation was carried out for the proposed methodology in order to evaluate its suitability for NRT operational service. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spills and look- alikes. Polarimetric features such as Scattering Diversity and Pauli-based features proved to be more discriminative than other polarimetric features.
Involved external institutions
Germany (DE)How to cite
APA:
Singha, S., Ressel, R., & Lehner, S. (2016). Multi-frequency and multi-polarization analysis of oil slicks using TerraSAR-X and RADARSAT-2 data. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 4035-4038). Beijing, CHN: Institute of Electrical and Electronics Engineers Inc..
MLA:
Singha, Suman, Rudolf Ressel, and Susanne Lehner. "Multi-frequency and multi-polarization analysis of oil slicks using TerraSAR-X and RADARSAT-2 data." Proceedings of the 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, CHN Institute of Electrical and Electronics Engineers Inc., 2016. 4035-4038.
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