NDLI: Remote sensing image feature selection based on α-torrent rough set theory

Content Provider	IEEE Xplore Digital Library
Author	Xin Pan SuLi Zhang
Copyright Year	2010
Description	Author affiliation: School of Electrical & Information Technology, Changchun Institute of Technology, China (Xin Pan; SuLi Zhang)
Abstract	Supervised classification in remote sensing imagery is receiving increasing attention in current research. In order to improve the classification ability, a lot of spatial-features have been utilized. Unfortunately, too many features often cause classifier over-fit to a certain features' character and lead to lower classification accuracy. Feature selection algorithms have utilized to select useful feature and improve classification accuracy. Rough set theory, as a powerful analysis tool, has been proven to be effective in remote sensing classification field. But spectral uncertainty or vagueness caused by spectral confusion between-class and spectral variation within-class leads to the overlap in a large number of features. In these cases, the traditional rough sets can not perform effectively. To solve this problem, this research proposed a new feature selection method based on α-Torrent rough set theory. The experiments showed, compared with PCA and traditional rough set method, that our method could select usefully features and improved classification accuracy.
Starting Page	1034
Ending Page	1038
File Size	785917
Page Count	5
File Format	PDF
ISBN	9781424459315
e-ISBN	9781424459346
DOI	10.1109/FSKD.2010.5569580
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy ±-torrent rough set Rough sets feature overlap remote sensing Classification algorithms Remote sensing feature selection Classification tree analysis Information systems
Content Type	Text
Resource Type	Article

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