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. OPEN ACCESSRemote Sens. 2013, 5 5090 Keywords: joint land cover mapping and registration; Markov random field; optimum classifier; mean field theory; EM algorithm
The traditional land cover mapping (LCM) algorithms assume that images are perfectly registered. In practice, this assumption may not always be valid since these images may be acquired from different sensor platforms, or at different time which may suffer small variations in platform flight paths. As a result, it is imperative to incorporate the registration error into the land cover mapping algorithm. In this paper, we propose a joint LCM and image registration algorithm under the Markov random field model. Here, the expectation-maximization algorithm is employed to search for the optimum LCM as well as the map parameters. Our result shows that the proposed MRF-Based approach can increase the accuracies of the classification maps as well as the map parameter estimation.Index Terms-Remote sensing, joint land cover mapping and registration, Markov random fields, EM algorithm
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.