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Speckle reduction of SAR images using neural networks

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3 Author(s)
Blacknell, D. ; Defence Res. Agency, UK ; Oliver, C.J. ; Warner, M.

Synthetic aperture radar (SAR) is a high-resolution remote sensing platform with all-weather capability. Traditional filter-based techniques are unsuitable for smoothing SAR images, but considerable success has been achieved using a CPU intensive, algorithmic noise removal process called simulated annealing. In order to reduce the CPU requirements of the despeckling process we have presented a solution based upon neural networks which are a form of adaptive filter. A variety of neural network architectures based on the multilayer perceptron and the vector quantizer network have been trained to learn the despeckling process. We have demonstrated that such a hybrid network can be successfully trained to perform speckle reduction of SAR images. The hybrid network benefits from reduced training and execution times compared to a single MLP, whilst maintaining a good performance

Published in:

Image Processing and its Applications, 1995., Fifth International Conference on

Date of Conference:

4-6 Jul 1995

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