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  Remote Sensing Feature Selection by Kernel Dependence Estimation

Camps-Valls, G., Mooij, J., & Schölkopf, B. (2010). Remote Sensing Feature Selection by Kernel Dependence Estimation. IEEE Geoscience and Remote Sensing Letters, 7(3), 587-591. doi:10.1109/LGRS.2010.2041896.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BF2A-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-756C-2
Genre: Journal Article

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 Creators:
Camps-Valls, G, Author              
Mooij, JM1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: This letter introduces a nonlinear measure of independence between random variables for remote sensing supervised feature selection. The so-called Hilbert–Schmidt independence criterion (HSIC) is a kernel method for evaluating statistical dependence and it is based on computing the Hilbert–Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is easy to compute and has good theoretical and practical properties. Rather than using this estimate for maximizing the dependence between the selected features and the class labels, we propose the more sensitive criterion of minimizing the associated HSIC p-value. Results in multispectral, hyperspectral, and SAR data feature selection for classification show the good performance of the proposed approach.

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 Dates: 2010-07
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1109/LGRS.2010.2041896
BibTex Citekey: 6263
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Title: IEEE Geoscience and Remote Sensing Letters
  Other : IEEE Geosci. Remote Sens. Lett.
Source Genre: Journal
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Publ. Info: Piscataway, NJ : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: 7 (3) Sequence Number: - Start / End Page: 587 - 591 Identifier: ISSN: 1545-598X
CoNE: https://pure.mpg.de/cone/journals/resource/954925491886