Title :
Neural networks for the oil spill detection using ERS-SAR data
Author :
Calabresi, G. ; Frate, F. Del ; Lichtenegger, J. ; Petrocchi, A. ; Trivero, P.
Author_Institution :
ESA/ESRIN, Rome, Italy
Abstract :
A neural network approach for semi-automatic detection of oil spills in ERS-SAR imagery is presented. The network input is a vector containing the values of a set of features, previously calculated by using dedicated routines, characterizing the oil spill candidate either from the point of view of its geometry or of its physical behaviour. The algorithm classification performance has been evaluated on a data set containing verified examples of oil spill and look-alike
Keywords :
environmental science computing; feature extraction; geophysical signal processing; geophysics computing; neural nets; oceanographic techniques; radar imaging; remote sensing by radar; spaceborne radar; synthetic aperture radar; water pollution measurement; ERS; SAR; SAR imagery; algorithm classification; feature extraction; marine pollution; measurement technique; neural net; neural network; oil pollution; oil slick; oil spill detection; radar imaging; radar remote sensing; semi-automatic detection; spaceborne radar; synthetic aperture radar; water pollution; Classification algorithms; Electronic mail; Image analysis; Neural networks; Petroleum; Pollution measurement; Radar detection; Remote monitoring; Sea measurements; Spaceborne radar;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International
Conference_Location :
Hamburg
Print_ISBN :
0-7803-5207-6
DOI :
10.1109/IGARSS.1999.773451
