Detection of oil leakage in SAR images using wavelet feature extractors and unsupervised neural classifiers

Citation
Cp. Lin et al., Detection of oil leakage in SAR images using wavelet feature extractors and unsupervised neural classifiers, IEICE TR CO, E83B(9), 2000, pp. 1955-1962
Citations number
23
Language
INGLESE
art.tipo
Article
Categorie Soggetti
Information Tecnology & Communication Systems
Journal title
IEICE TRANSACTIONS ON COMMUNICATIONS
ISSN journal
0916-8516 → ACNP
Volume
E83B
Issue
9
Year of publication
2000
Pages
1955 - 1962
Database
ISI
SICI code
0916-8516(200009)E83B:9<1955:DOOLIS>2.0.ZU;2-P
Abstract
A new algorithm based on wavelets and neural networks is proposed For discr iminating oil leaks using synthetic aperture radar (SAR) images. Utilizing the advantages of wavelets and neural networks, the algorithm is speedy and effective to distinguish oil embedded in both sea clutter and land clutter . The iterative algorithm uses a wavelet feature extractor and two unsuperv ised neural classifiers. The first stage classifier can divide the pixels i n the SAR image into sea water, land and oil clusters. In the second stage, the classifier extracts oil pixels from previous oil cluster until matchin g the characteristics of the oil template. Using our proposed algorithm, th e oil cluster will be formed automatically, provided the desired oil templa te is defined in advance.