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Proceedings Paper

Data fusion in a Markov random-field-based image segmentation approach
Author(s): Paul C. Smits; Silvana G. Dellepiane
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Paper Abstract

Synthetic aperture radar data may contain useful information about small structures like rivers and man made structures. Using the Markov random field based segmentation algorithms, that perform quite well on homogeneously textured areas (i.e., agriculture land cover), these structures may be lost if they are small (1-2 pixel wide). The merging of various sources of information at a low level of the Markov random field region label process, makes it possible to recover at least partly the fine structures in the SAR data. The data fusion makes use of Bayesian inference about the Markovian property of neighborhood systems. This article shows that the proposed method is valid and technically feasible, based on extensive validations.

Paper Details

Date Published: 17 December 1996
PDF: 11 pages
Proc. SPIE 2955, Image and Signal Processing for Remote Sensing III, (17 December 1996); doi: 10.1117/12.262877
Show Author Affiliations
Paul C. Smits, Univ. of Genoa (Italy)
Silvana G. Dellepiane, Univ. of Genoa (Italy)


Published in SPIE Proceedings Vol. 2955:
Image and Signal Processing for Remote Sensing III
Jacky Desachy, Editor(s)

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