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Proceedings Paper

Classification of multisource imagery based on a Markov random field model
Author(s): Anne H. Schistad Solberg; Torfin Taxt
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Paper Abstract

In this paper, a general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (geographic information systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependency context between neighboring pixels in an image, and temporal class dependency context between the different images. The performance of the specific model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land- use classification. The MRF model performs significantly better than a simpler reference fusion model it is compared to.

Paper Details

Date Published: 30 December 1994
PDF: 10 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196730
Show Author Affiliations
Anne H. Schistad Solberg, Norwegian Computing Ctr. (Norway)
Torfin Taxt, Univ. of Oslo (Norway)


Published in SPIE Proceedings Vol. 2315:
Image and Signal Processing for Remote Sensing
Jacky Desachy, Editor(s)

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