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Optical Engineering

Classification of multispectral images through a rough-fuzzy neural network
Author(s): Chi-Wu Mao; Shao-Han Liu; Jzau-Sheng Lin
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Paper Abstract

A new fuzzy Hopfield-model net based on rough-set reasoning is proposed for the classification of multispectral images. The main purpose is to embed a rough-set learning scheme into the fuzzy Hopfield network to construct a classification system called a rough-fuzzy Hopfield net (RFHN). The classification system is a paradigm for the implementation of fuzzy logic and rough systems in neural network architecture. Instead of all the information in the image being fed into the neural network, the upper- and lower-bound gray levels, captured from a training vector in a multispectal image, are fed into a rough-fuzzy neuron in the RFHN. Therefore, only 2/N pixels are selected as the training samples if an N-dimensional multispectral image was used. In the simulation results, the proposed network not only reduces the consuming time but also reserves the classification performance.

Paper Details

Date Published: 1 January 2004
PDF: 10 pages
Opt. Eng. 43(1) doi: 10.1117/1.1629685
Published in: Optical Engineering Volume 43, Issue 1
Show Author Affiliations
Chi-Wu Mao, National Cheng Kung Univ. (Taiwan)
Shao-Han Liu, National Chin-Yi Institute of Technology (Taiwan)
Jzau-Sheng Lin, National Chinyi Institute of Technology (Taiwan)

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