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Pattern Analysis and Applications

Erschienen in:

29.08.2015 | Theoretical Advances

High-dimensional image data feature extraction by double discriminant embedding

verfasst von: Maryam Imani, Hassan Ghassemian

Erschienen in: Pattern Analysis and Applications | Ausgabe 2/2017

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Abstract

We propose a supervised feature extraction method in this paper that uses two successive transformations to produce the extracted features. The first projection maximizes the difference between spectral features. Thus, produced features have minimum overlap in the new feature space. The second projection maximizes the discrimination between classes. The proposed method, which is called double discriminant embedding (DDE), uses just the first statistics of data. Thus, DDE has good efficiency using limited training samples. The experimental results on four popular hyperspectral images show the better efficiency of DDE in comparison with LDA, GDA, NWFE, and supervised LPP methods in small sample size situation.

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Titel: High-dimensional image data feature extraction by double discriminant embedding
verfasst von: Maryam Imani
Hassan Ghassemian
Publikationsdatum: 29.08.2015
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 2/2017
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-015-0513-z

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