Classification of multisource imagery based on a Markov random field model

Anne H. Schistad Solberg; Torfin Taxt

doi:10.1117/12.196730

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30 December 1994 Classification of multisource imagery based on a Markov random field model

Anne H. Schistad Solberg, Torfin Taxt

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Proceedings Volume 2315, Image and Signal Processing for Remote Sensing; (1994) https://doi.org/10.1117/12.196730
Event: Satellite Remote Sensing, 1994, Rome, Italy

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.

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Anne H. Schistad Solberg and Torfin Taxt "Classification of multisource imagery based on a Markov random field model", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); https://doi.org/10.1117/12.196730

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