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Proceedings Paper

A neural network detection model of spilled oil based on the texture analysis of SAR image
Author(s): Jubai An; Lisong Zhu
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Paper Abstract

A Radial Basis Function Neural Network (RBFNN) Model is investigated for the detection of spilled oil based on the texture analysis of SAR imagery. In this paper, to take the advantage of the abundant texture information of SAR imagery, the texture features are extracted by both wavelet transform and the Gray Level Co-occurrence matrix. The RBFNN Model is fed with a vector of these texture features. The RBFNN Model is trained and tested by the sample data set of the feature vectors. Finally, a SAR image is classified by this model. The classification results of a spilled oil SAR image show that the classification accuracy for oil spill is 86.2 by the RBFNN Model using both wavelet texture and gray texture, while the classification accuracy for oil spill is 78.0 by same RBFNN Model using only wavelet texture as the input of this RBFNN model. The model using both wavelet transform and the Gray Level Co-occurrence matrix is more effective than that only using wavelet texture. Furthermore, it keeps the complicated proximity and has a good performance of classification.

Paper Details

Date Published: 2 February 2006
PDF: 7 pages
Proc. SPIE 6031, ICO20: Remote Sensing and Infrared Devices and Systems, 60310Y (2 February 2006); doi: 10.1117/12.668036
Show Author Affiliations
Jubai An, Dalian Maritime Univ. (China)
Lisong Zhu, Dalian Maritime Univ. (China)


Published in SPIE Proceedings Vol. 6031:
ICO20: Remote Sensing and Infrared Devices and Systems
Jingshan Jiang; O. Yu. Nosach; Jiaqi Wang, Editor(s)

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