NDLI: Automatic recognition of coastal and oceanic environmental events with orbital radars

Content Provider	IEEE Xplore Digital Library
Author	Bentz, C.M. Politano, A.T. Ebecken, N.F.F.
Copyright Year	2007
Description	Author affiliation: PETROBRAS - R&D Center, Rio de Janeiro (Bentz, C.M.; Politano, A.T.)
Abstract	An automatic classification procedure was developed able to identify different oceanic events, detectable in orbital radar images. The procedure was customized to be used in the southeastern Brazilian coast, since the classification training and test used examples extracted from 402 RADARSAT-1 images acquired in this region. Different sets of spectral, geometric and contextual (meteo-oceanographic and location) features of selected low backscatter patches were evaluated. Machine learning procedures (neural networks, decision trees and support vector machines) were used to induce classifiers to differentiate between seven classes, belonging to two categories. The classification procedure involves two steps: first the features area classified in one of two categories - oil spill or meteo- oceanographic phenomena. In the second step, the identification of tree classes of oil spills and four classes of meteo- oceanographic phenomena is done. The oil spill related classes are associated to operational exploration and production spills, ship releases and others. The meteo-oceanographic phenomena include biogenic oils and/or upwellings, algae blooms, low wind areas and rain cells. The models induced by support vector machines and neural networks achieved good results, allowing the operational implementation of the proposed procedures.
Starting Page	914
Ending Page	916
File Size	1281257
Page Count	3
File Format	PDF
ISBN	9781424412112
DOI	10.1109/IGARSS.2007.4422946
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sea measurements Petroleum Radar imaging Neural networks Support vector machines Radar detection Event detection Testing Backscatter Machine learning machine learning Synthetic aperture radar oil spill ocean features detection classification
Content Type	Text
Resource Type	Article

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