A neural network detection model of spilled oil based on the texture analysis of SAR image

Jubai An; Lisong Zhu

doi:10.1117/12.668036

2 February 2006 A neural network detection model of spilled oil based on the texture analysis of SAR image

Jubai An, Lisong Zhu

Proceedings Volume 6031, ICO20: Remote Sensing and Infrared Devices and Systems; 60310Y (2006) https://doi.org/10.1117/12.668036
Event: ICO20:Optical Devices and Instruments, 2005, Changchun, China

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.

Citation Download Citation

Jubai An and Lisong Zhu "A neural network detection model of spilled oil based on the texture analysis of SAR image", Proc. SPIE 6031, ICO20: Remote Sensing and Infrared Devices and Systems, 60310Y (2 February 2006); https://doi.org/10.1117/12.668036

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KEYWORDS

Synthetic aperture radar

Neural networks

Image classification

Wavelets

Wavelet transforms

Feature extraction

Image analysis

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