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- W2019929754 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." @default.
- W2019929754 created "2016-06-24" @default.
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- W2019929754 date "2010-08-01" @default.
- W2019929754 modified "2023-09-26" @default.
- W2019929754 title "Remote sensing image feature selection based on α-torrent rough set theory" @default.
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- W2019929754 doi "https://doi.org/10.1109/fskd.2010.5569580" @default.
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