1 January 2004 Classification of multispectral images through a rough-fuzzy neural network
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Optical Engineering, 43(1), (2004). doi:10.1117/1.1629685
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.
Chi-Wu Mao, Shao-Han Liu, Jzau-Sheng Lin, "Classification of multispectral images through a rough-fuzzy neural network," Optical Engineering 43(1), (1 January 2004). http://dx.doi.org/10.1117/1.1629685
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KEYWORDS
Neurons

Multispectral imaging

Image classification

Fuzzy logic

Neural networks

Image segmentation

Magnetic resonance imaging

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