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Proceedings Paper

Combining variable precision rough set and neural network in remote sensing image classification
Author(s): Qiong Wang; Jian Liu
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Paper Abstract

This paper presents a new approach of Remotely Sensed data classification based on Variable Precision Rough set(VPRS) and BP neural network, compared to traditional rough sets, VPRS is more robust to noise and can generate more concise and representative classification rules of the remote sensing image. After the rules are deduced, they are fed to the BP neural network, which results in short training time and a high classification accuracy.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960P (30 October 2009); doi: 10.1117/12.831345
Show Author Affiliations
Qiong Wang, Huazhong Univ. of Science and Technology (China)
Jian Liu, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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