Semisupervised Feature Selection for Unbalanced Sample Sets of VHR Images
IEEE Geoscience and Remote Sensing LettersPublished 3 June 2010
Xi Chen, Tao Fang, Hong Huo, Deren Li
Citations15
SJR quartileQ1
SJR score1.26
SNIP1.37
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TL;DR
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.
Abstract
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 incorporates asymmetric misclassification costs of classes into weight matrices. Furthermore, this method locally exploits multiple kinds of relationships between sample pairs to more accurately assess the ability of features in preserving the geometrical and discriminant structures. The experimental results on VHR satellite and airborne imagery attest to the effectiveness and practicability of ALDS.
Keywords
ChemistryComputer ScienceEngineering
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