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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Algoritma baharu berdasarkan wavelet dan rangkaian saraf dicadangkan untuk mendiskriminasi kebocoran minyak menggunakan imej radar apertur sintetik (SAR). Menggunakan kelebihan wavelet dan rangkaian saraf, algoritma ini pantas dan berkesan untuk membezakan minyak yang tertanam dalam kedua-dua kekusutan laut dan kekusutan darat. Algoritma lelaran menggunakan pengekstrak ciri wavelet dan dua pengelas saraf tanpa pengawasan. Pengelas peringkat pertama boleh membahagikan piksel dalam imej SAR kepada gugusan air laut, darat dan minyak. Pada peringkat kedua, pengelas mengekstrak piksel minyak daripada gugusan minyak sebelumnya sehingga memadankan ciri templat minyak. Menggunakan algoritma yang dicadangkan kami, gugusan minyak akan terbentuk secara automatik, dengan syarat templat minyak yang dikehendaki ditentukan terlebih dahulu.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
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
Salinan
@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},}
Salinan
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 -