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Adjacency selection in Markov Random Fields for high spatial resolution hyperspectral data

Author

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  • Francesco Lagona

    (Department of Social Sciences, University “Roma Tre”, via Corrado Segre 4, 00146 Rome, Italy (e-mail: lagona@uniroma3.it))

Abstract

. Markov Random Fields, implemented for the analysis of remote sensing images, capture the natural spatial dependence between band wavelengths taken at each pixel, through a suitable adjacency relationship between pixels, to be defined a priori. In most cases several adjacency definitions seem viable and a model selection problem arises. A BIC-penalized Pseudo-Likelihood criterion is suggested which combines good distributional properties and computational feasibility for analysis of high spatial resolution hyperspectral images. Its performance is compared with that of the BIC-penalized Likelihood criterion for detecting spatial structures in a high spatial resolution hyperspectral image for the Lamar area in Yellowstone National Park.

Suggested Citation

  • Francesco Lagona, 2002. "Adjacency selection in Markov Random Fields for high spatial resolution hyperspectral data," Journal of Geographical Systems, Springer, vol. 4(1), pages 53-68, March.
    • Handle: RePEc:kap:jgeosy:v:4:y:2002:i:1:d:10.1007_s101090100074
    DOI: 10.1007/s101090100074
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    Cited by:

    1. Giovanna Jona Lasinio & Francesco Lagona, 2002. "Selection of the neighborhood structure for space-time Markov random field models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(3), pages 293-311, October.

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