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| An Improved Approach to Change Detection in Multitemporal Remote-Sensing Images |
| Yuan Qi Zhao Rong-chun |
| Department of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China |
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Abstract Traditional unsupervised change detection algorithms based on simple MRF model assume that subimages applied to extracting features are homogeneous, but that is not always true and causes low accuracy. Based on the fields Correlation Markov Random Field (CMRF) model, an adaptive algorithm is proposed in this paper. The labeling is obtained through solving a Maximum A Posterior (MAP) problem by Iteration Condition Model (ICM). Features of each pixel are exacted by using only the pixels currently labeled as the same pattern. With the adapted features, the new labeling is obtained. Under the idea of two-stage iteration algorithm, we use the difference image of multitemporal remote-sensing images as observation field. The satisfied experimental confirm the effectiveness of proposed techniques.
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Received: 23 April 2007
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Corresponding Authors:
Yuan Qi
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2008, Vol. 30 
