NDLI: A application of OP-ELM in the remote sensing images recognization

Content Provider	IEEE Xplore Digital Library
Author	Wen Changji Wang Zenghui Su Hengqiang Zhou Cuijuan
Copyright Year	2012
Description	Author affiliation: School of Information Technology, Jilin Agricultural University, Changchun 130118, China (Wen Changji; Wang Zenghui; Su Hengqiang; Zhou Cuijuan)
Abstract	Automatic recognization of remote sensing images is significant. In this article, we use optimally pruned extreme learning machine(OP-ELM) as an automatic classifier to achieve recognization of the objects in remote sensing images based on texture features through grey-level co-occurrence matrix method. OP-ELM is based on the original extreme learning machine(ELM) algorithm with additional steps to make it more robust and genetic. For the recognization, several co-occurrence parameters are computed and compared, and then, we used the more obvious features of the texture of the objects in remote sensing images as criterions. To improve the effection of recognization, we bring in threshold filter, erosion filter and so on after the OP-ELM classification. In the experiments, the results for both computational time and accuracy(Kappa coefficients) are compared to the SVM and BP. As the illustration, OP-ELM performs faster than the other algorithm Without losing the accuracy of the premise.
Starting Page	1
Ending Page	4
File Size	828012
Page Count	4
File Format	PDF
ISBN	9781467344975
ISSN	21544824
e-ISBN	9781889334479
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-24
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	TSI Press
Subject Keyword	Remote sensing Machine learning Image recognition Classification algorithms Accuracy Neurons Approximation algorithms recognization optimally pruned extreme learning machine(OP-ELM) texture feature Classification Gray-level Co-occurrence Matrices
Content Type	Text
Resource Type	Article

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