Neural networks for oil spill detection using ERS-SAR data

Author

Frate, Fabio Del ; Petrocchi, Andrea ; Lichtenegger, Juerg ; Calabresi, Gianna

Author_Institution

Vergata Univ., Rome, Italy

Volume

38

Issue

5

fYear

2000

fDate

9/1/2000 12:00:00 AM

Firstpage

2282

Lastpage

2287

Abstract

A neural network approach for semi-automatic detection of oil spills in European remote sensing satellite-synthetic aperture radar (ERS-SAR) imagery is presented. The network input is a vector containing the values of a set of features characterizing an oil spill candidate. The classification performance of the algorithm has been evaluated on a data set containing verified examples of oil spill and look-alike. A direct analysis of the information content of the calculated features has been also carried out through an extended pruning procedure of the net

Keywords

feature extraction; geophysical signal processing; geophysics computing; image classification; neural nets; oceanographic techniques; radar imaging; remote sensing by radar; water pollution measurement; ERS; SAR; algorithm; extended pruning procedure; feature extraction; image classification; image processing; marine pollution; measurement technique; neural net; neural network; oil slick; oil spill; radar imaging; radar remote sensing; semi-automatic detection; spaceborne radar; water pollution; Classification algorithms; Neural networks; Petroleum; Radar detection; Radar imaging; Radar remote sensing; Remote monitoring; Remote sensing; Spaceborne radar; Viscosity;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/36.868885

Filename

868885