Remote Sensing Image Fusion Based on Optimized Dictionary Learning

In order to improve the fusion quality of panchromatic image and multi-spectral image, a remote sensing image fusion method based on optimized dictionary learning is proposed. Firstly, K-means cluster is applied to image blocks in the image database, and then image blocks with high similarity are removed partly in order to improve the training efficiency. While obtaining a universal dictionary, the similar dictionary atoms and less used dictionary atoms are marked for further research. Secondly, similar dictionary atoms and less used dictionary atoms are replaced by panchromatic image blocks with the largest difference from the original sparse model to obtain an adaptive dictionary. Furthermore the adaptive dictionary is used to sparse represent the intensity component and panchromatic image, the modulus maxima coefficients in the sparse coefficients of each image blocks are separated to obtain maximal sparse coefficients, and the remaining sparse coefficients are called residual sparse coefficients. Then, each part is fused by different fusion rules to preserve more spectral and spatial detail information. Finally, inverse IHS transform is employed to obtain the fused image. Experiments demonstrate that the proposed method provides better spectral quality and superior spatial information in the fused image than its counterparts.