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Localized empirical discriminant analysis

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  • Cutillo, L.
  • Amato, U.

Abstract

Some empirical localized discriminant analysis methods for classifying images are introduced. They use spatial correlation of images in order to improve classification reducing the 'pseudo-nuisance' present in pixel-wise discriminant analysis. The result is obtained through an empirical (data driven) and local (pixel-wise) choice of the prior class probabilities. Local empirical discriminant analysis is formalized in a framework that focuses on the concept of visibility of a class that is introduced. Numerical experiments are performed on synthetic and real data. In particular, methods are applied to the problem of retrieving the cloud mask from remotely sensed images. In both cases classical and new local discriminant methods are compared to the ICM method.

Suggested Citation

  • Cutillo, L. & Amato, U., 2008. "Localized empirical discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4966-4978, July.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:11:p:4966-4978
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    References listed on IDEAS

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    1. Kolaczyk, Eric D. & Ju, Junchang & Gopal, Sucharita, 2005. "Multiscale, Multigranular Statistical Image Segmentation," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1358-1369, December.
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    Cited by:

    1. Nielsen, Jens D. & Rumí, Rafael & Salmerón, Antonio, 2009. "Supervised classification using probabilistic decision graphs," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1299-1311, February.
    2. Jacek Batog & Barbara Batog, 2021. "Typology and Development of Local Administrative Units: Spatial Discriminant Analysis," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 548-569.

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