SAR and Optical Image Fusion Based on Contourlet Hidden Markov Tree Model

Contourlet-domain hidden markov tree(HMT) model can reflect the coefficients' dependencies of different scales and directions, then a SAR and optical image fusion algorithm based on it is proposed. Firstly source images are decomposed by Contourlet transform, low frequency and high frequency subband coefficients are obtained; secondly, the high frequency subband coefficients are modeled using hidden markov tree and the model is trained using Expectation-Maximization(EM) algorithm to get the posterior probability of the coefficients; Thirdly, using the posterior probability to guide the fusion rules' design of high frequency subband coefficients in order to preserve the salient features of original images well and obtain good noise suppression; Finally, the fusion coefficients are inversed to get the final fusion result. SAR and Panchromatic images were taken to fuse, and the results were evaluated by Difference Coefficient, Correlation Coefficient and Signal-Noise Ration. The experimental result shows that the proposed algorithm is better than traditional fusion algorithms based on Contourlet and wavelet-domain hidden markov tree model.