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

Francesco Lagona
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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)

Journal of Geographical Systems, 2002, vol. 4, issue 1, 53-68

Abstract: 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.

Keywords: Key words: Adjacency selection; Bayesian penalization rate; hyperspectral data; pseudo-likelihood; Markov random fields; JEL classification: C12; C63 (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s101090100074

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