DocumentCode :
1284581
Title :
An Image Fusion Approach Based on Markov Random Fields
Author :
Xu, Min ; Chen, Hao ; Varshney, Pramod K.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA
Volume :
49
Issue :
12
fYear :
2011
Firstpage :
5116
Lastpage :
5127
Abstract :
Markov random field (MRF) models are powerful tools to model image characteristics accurately and have been successfully applied to a large number of image processing applications. This paper investigates the problem of fusion of remote sensing images, e.g., multispectral image fusion, based on MRF models and incorporates the contextual constraints via MRF models into the fusion model. Fusion algorithms under the maximum a posteriori criterion are developed to search for solutions. Our algorithm is applicable to both multiscale decomposition (MD)-based image fusion and non-MD-based image fusion. Experimental results are provided to demonstrate the improvement of fusion performance by our algorithms.
Keywords :
Markov processes; geophysical image processing; geophysical techniques; image fusion; remote sensing; MRF models; Markov random field models; fusion algorithms; fusion model; image characteristics; image processing applications; maximum a posteriori criterion; multiscale decomposition based image fusion; multispectral image fusion; nonMD-based image fusion; remote sensing image fusion; Correlation; Image fusion; Image resolution; Markov random fields; Transforms; Markov random field; multi-resolution decomposition; multispectral image fusion;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2011.2158607
Filename :
5963713
Link To Document :
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