NDLI: Classification of multispectral images using neuro-statistical classifier based on decision fusion and feature fusion

Content Provider	IEEE Xplore Digital Library
Author	Venkatalakshmi, K. MercyShalinie, S.
Copyright Year	2004
Description	Author affiliation: Dept. of IT, Thiagarajar Coll. of Eng., Madurai, India (Venkatalakshmi, K.)
Abstract	Artificial neural networks have gained increasing popularity as an alternative to statistical methods for classification of remote sensed images. The superiority of neural networks is that if they are trained with representative training samples they show improvement over statistical methods in terms of overall accuracies. However if the distribution functions of the information classes are known, statistical classification algorithms work very well. To retain the advantages of both the classifiers, decision fusion is used to integrate the decisions of the individual classifiers. To overcome the difficulty of classification in high dimensional feature space feature fusion is done to reduce the dimensionality. AVIRIS images are used as test site and classification is initially achieved using maximum likelihood classifier followed by a set of neural network classifiers which include perceptron, hamming and hopfield networks. The decisions of these classifiers are fused in the decision fusion center implemented using second perceptron network. The results show that the scheme is effective in terms of increased classification accuracies.
Sponsorship	Faculty of Eng., Univ. of Melbourne IEEE Madras Section, Chennai
Starting Page	283
Ending Page	288
File Size	495703
Page Count	6
File Format	PDF
ISBN	0780382439
DOI	10.1109/ICISIP.2004.1287668
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-01-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multispectral imaging Statistical analysis Neural networks Artificial neural networks Feature extraction Educational institutions Electronic mail Pattern recognition Data mining Remote sensing
Content Type	Text
Resource Type	Article

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