The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Un nouvel algorithme basé sur des ondelettes et des réseaux de neurones est proposé pour discriminer les fuites de pétrole à l'aide d'images radar à synthèse d'ouverture (SAR). Utilisant les avantages des ondelettes et des réseaux neuronaux, l’algorithme est rapide et efficace pour distinguer le pétrole intégré à la fois dans le fouillis marin et dans le fouillis terrestre. L'algorithme itératif utilise un extracteur de caractéristiques d'ondelettes et deux classificateurs neuronaux non supervisés. Le classificateur de première étape peut diviser les pixels de l’image SAR en groupes d’eau de mer, de terre et de pétrole. Dans la deuxième étape, le classificateur extrait les pixels d'huile du groupe d'huile précédent jusqu'à ce qu'ils correspondent aux caractéristiques du modèle d'huile. Grâce à l'algorithme que nous proposons, le cluster d'huile sera formé automatiquement, à condition que le modèle d'huile souhaité soit défini à l'avance.
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Chih-ping LIN, Motoaki SANO, Shinzo OBI, Shuji SAYAMA, Matsuo SEKINE, "Detection of Oil Leakage in SAR Images Using Wavelet Feature Extractors and Unsupervised Neural Classifiers" in IEICE TRANSACTIONS on Communications,
vol. E83-B, no. 9, pp. 1955-1962, September 2000, doi: .
Abstract: A new algorithm based on wavelets and neural networks is proposed for discriminating 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 unsupervised neural classifiers. The first stage classifier can divide the pixels in the SAR image into sea water, land and oil clusters. In the second stage, the classifier extracts oil pixels from previous oil cluster until matching the characteristics of the oil template. Using our proposed algorithm, the oil cluster will be formed automatically, provided the desired oil template is defined in advance.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e83-b_9_1955/_p
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@ARTICLE{e83-b_9_1955,
author={Chih-ping LIN, Motoaki SANO, Shinzo OBI, Shuji SAYAMA, Matsuo SEKINE, },
journal={IEICE TRANSACTIONS on Communications},
title={Detection of Oil Leakage in SAR Images Using Wavelet Feature Extractors and Unsupervised Neural Classifiers},
year={2000},
volume={E83-B},
number={9},
pages={1955-1962},
abstract={A new algorithm based on wavelets and neural networks is proposed for discriminating 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 unsupervised neural classifiers. The first stage classifier can divide the pixels in the SAR image into sea water, land and oil clusters. In the second stage, the classifier extracts oil pixels from previous oil cluster until matching the characteristics of the oil template. Using our proposed algorithm, the oil cluster will be formed automatically, provided the desired oil template is defined in advance.},
keywords={},
doi={},
ISSN={},
month={September},}
Copier
TY - JOUR
TI - Detection of Oil Leakage in SAR Images Using Wavelet Feature Extractors and Unsupervised Neural Classifiers
T2 - IEICE TRANSACTIONS on Communications
SP - 1955
EP - 1962
AU - Chih-ping LIN
AU - Motoaki SANO
AU - Shinzo OBI
AU - Shuji SAYAMA
AU - Matsuo SEKINE
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E83-B
IS - 9
JA - IEICE TRANSACTIONS on Communications
Y1 - September 2000
AB - A new algorithm based on wavelets and neural networks is proposed for discriminating 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 unsupervised neural classifiers. The first stage classifier can divide the pixels in the SAR image into sea water, land and oil clusters. In the second stage, the classifier extracts oil pixels from previous oil cluster until matching the characteristics of the oil template. Using our proposed algorithm, the oil cluster will be formed automatically, provided the desired oil template is defined in advance.
ER -