Classification of multipolarized SAR images by an unsupervised back-propagation neural network with texture discrimination

Lili Chen; Jun Hong; Baohong Li

doi:10.1117/12.323572

25 September 1998 Classification of multipolarized SAR images by an unsupervised back-propagation neural network with texture discrimination

Lili Chen, Jun Hong, Baohong Li

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Proceedings Volume 3545, International Symposium on Multispectral Image Processing (ISMIP'98); (1998) https://doi.org/10.1117/12.323572
Event: International Symposium on Multispectral Image Processing, 1998, Wuhan, China

Abstract

An unsupervised back-propagation neural network with texture discrimination is proposed for classification of multi- polarized SAR images. The first improvement of this method is to design an unsupervised training process for the back- propagation network. We use clustering methods to form initial clusters. Then a moving windows is used to pick the training sets automatically. By such preprocess the back- propagation network is unsupervised overall. Another improvement in this method is that, except for radiometric information, we integrate pixel texture into neural network. The parameters of pixel texture are calculated by gray level difference statistics in the preprocess phase and then are input to the nodes with gray-level of a pixel. The improved network is used to classify multi-polarized SAR data. We compare its result with that of BP network which use radiometric information only. The result shows that this method is effective and the classification performance is improved.

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Lili Chen, Jun Hong, and Baohong Li "Classification of multipolarized SAR images by an unsupervised back-propagation neural network with texture discrimination", Proc. SPIE 3545, International Symposium on Multispectral Image Processing (ISMIP'98), (25 September 1998); https://doi.org/10.1117/12.323572

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KEYWORDS

Image classification

Neural networks

Synthetic aperture radar

Classification systems

Radio propagation

Distributed computing

Electronics

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