Neural networks for oil spill detection using ERS-SAR data

被引：228

作者：

Del Frate, F ^{[1
]}

Petrocchi, A

Lichtenegger, J

Calabresi, G

机构：

[1] Univ Roma Tor Vergata, I-00133 Rome, Italy

[2] ESA, Vitrociset, ESRIN, I-00044 Frascati, Italy

[3] ESA, ESRIN, I-00044 Frascati, Italy

来源：

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2000年 / 38卷 / 05期

关键词：

ERS-synthetic aperture radar (SAR); neural networks; oil spill detection;

D O I：

10.1109/36.868885

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

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.

引用

页码：2282 / 2287

页数：6

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