Semisupervised Feature Selection for Unbalanced Sample Sets of VHR Images

A semisupervised feature selection method, named asymmetrically local discriminant selection (ALDS), is proposed to evaluate the class separability of unbalanced sample sets from very high resolution (VHR) imagery in an object-oriented classification. In order to cope with class imbalance, ALDS inco...

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Vydané v:	IEEE geoscience and remote sensing letters Ročník 7; číslo 4; s. 781 - 785
Hlavní autori:	Chen, Xi, Fang, Tao, Huo, Hong, Li, Deren
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.10.2010 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Asymmetrically local discriminant selection (ALDS) Asymmetry class imbalance Classification Costs Face recognition Filters graph-based filter model Image processing Image resolution Imagery Machine learning Mathematical analysis Object oriented Object oriented modeling Object-oriented programming objectoriented classification Remote sensing Research and development Satellites semisupervised feature selection
ISSN:	1545-598X, 1558-0571
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