The aim of this paper is to introduce new algorithms for the oil spill detection taking fully advantage of the polarimetric and textural features contained in new generation SAR data such as those provided by Radarsat-2 and COSMO-SkyMed missions. The SAR information is exploited using a new statistical decomposition method based on AANN. Thanks to the AANN the original image is represented in terms of Nonlinear principal components (NLPC). The oil spill detection procedure is then directly applied to the new generated components
DEL FRATE, F., Latini, D., Scappiti, V. (2017). On Neural Networks Algorithms for Oil Spill Detetction when Applied to C- and X-Band SAR. In Proceedings of International Geoscience and Remote Sensing Symposium. IEEE [10.1109/IGARSS.2017.8128185].
On Neural Networks Algorithms for Oil Spill Detetction when Applied to C- and X-Band SAR
Fabio Del Frate;Daniele Latini;SCAPPITI, VALENTINA
2017-01-01
Abstract
The aim of this paper is to introduce new algorithms for the oil spill detection taking fully advantage of the polarimetric and textural features contained in new generation SAR data such as those provided by Radarsat-2 and COSMO-SkyMed missions. The SAR information is exploited using a new statistical decomposition method based on AANN. Thanks to the AANN the original image is represented in terms of Nonlinear principal components (NLPC). The oil spill detection procedure is then directly applied to the new generated components| File | Dimensione | Formato | |
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