For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Detection of Oil Leakage in SAR Images Using Wavelet Feature Extractors and Unsupervised Neural Classifiers
Chih-ping LIN Motoaki SANO Shinzo OBI Shuji SAYAMA Matsuo SEKINE
IEICE TRANSACTIONS on Communications
Publication Date: 2000/09/25
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Issue on Advances in Radar Systems)
oil leaks, SAR, wavelet, neural network,
Full Text: PDF(1.5MB)>>
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.