Share Email Print
cover

Proceedings Paper

Classification of multipolarized SAR images by an unsupervised back-propagation neural network with texture discrimination
Author(s): Lili Chen; Jun Hong; Baohong Li
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 25 September 1998
PDF: 4 pages
Proc. SPIE 3545, International Symposium on Multispectral Image Processing (ISMIP'98), (25 September 1998); doi: 10.1117/12.323572
Show Author Affiliations
Lili Chen, Institute of Electronics (China)
Jun Hong, Institute of Electronics (China)
Baohong Li, Institute of Electronics (China)


Published in SPIE Proceedings Vol. 3545:
International Symposium on Multispectral Image Processing (ISMIP'98)
Ji Zhou; Anil K. Jain; Tianxu Zhang; Yaoting Zhu; Mingyue Ding; Jianguo Liu, Editor(s)

© SPIE. Terms of Use
Back to Top