Remote Sens. 2013, 5(10), 5089-5121; doi:10.3390/rs5105089
Article

A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model

1 Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand 2 GISTDA, Bangkok 10210, Thailand 3 ICTES Program, Kasetsart University, Bangkok 10900, Thailand 4 BIOTECH, NSTDA, Pathumthani 12120, Thailand
* Author to whom correspondence should be addressed.
Received: 25 August 2013; in revised form: 19 September 2013 / Accepted: 23 September 2013 / Published: 15 October 2013
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Abstract: Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. As a result, a proper land cover mapping algorithm must be able to correct registration errors as well as perform an accurate classification. In this paper, we propose a joint classification and registration technique based on a Markov random field (MRF) model to simultaneously align two or more images and obtain a land cover map (LCM) of the scene. The expectation maximization (EM) algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP) criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy that is as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced.
Keywords: joint land cover mapping and registration; Markov random field; optimum classifier; mean field theory; EM algorithm

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MDPI and ACS Style

Kasetkasem, T.; Rakwatin, P.; Sirisommai, R.; Eiumnoh, A. A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model. Remote Sens. 2013, 5, 5089-5121.

AMA Style

Kasetkasem T, Rakwatin P, Sirisommai R, Eiumnoh A. A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model. Remote Sensing. 2013; 5(10):5089-5121.

Chicago/Turabian Style

Kasetkasem, Teerasit; Rakwatin, Preesan; Sirisommai, Ratchawit; Eiumnoh, Apisit. 2013. "A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model." Remote Sens. 5, no. 10: 5089-5121.

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