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High-dimensional image data feature extraction by double discriminant embedding

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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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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Tarbiat Modares University, P.O. Box 14155-111, Tehran, Iran

    Maryam Imani & Hassan Ghassemian

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  1. Maryam Imani
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  2. Hassan Ghassemian
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Correspondence to Hassan Ghassemian.

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Imani, M., Ghassemian, H. High-dimensional image data feature extraction by double discriminant embedding. Pattern Anal Applic 20, 473–484 (2017). https://doi.org/10.1007/s10044-015-0513-z

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  • DOI: https://doi.org/10.1007/s10044-015-0513-z

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