利用小波域HMC模型进行遥感图像变化检测

doi:10.3969/j.issn.1001-2400.2012.03.007

Abstract

Abstract:

The traditional threshold algorithms detect the changes in multitemporal remote sensing images based on the analysis of the signal function model, which has a poor accuracy for difference images with complex distribution. In this paper, a new approach is proposed by virtue of the double Gaussian mixture model and the wavelet transform. The proposed algorithm has better matching than the signal function model and introduces the spatial information by using the wavelet transform. After using the double Gaussian mixture models to detect the changed regions, the change maps in different scales are fused using the HMC model based on sequential maximum a posteriori estimation. The experiments on the real remote sensing images confirm the effectiveness of the proposed algorithm.

Key words: change detection, double Gaussian mixture model, wavelet transform, hidden Markov chain models

CLC Number:

TP751

XIN Fangfang;JIAO Licheng;WANG Guiting;WAN Honglin. Change detection in multi-temporal remote sensing images based on the wavelet-domain hidden Markov chain model[J].J4, 2012, 39(3): 43-49.

References

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Change detection in multi-temporal remote sensing images based on the wavelet-domain hidden Markov chain model

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