Remote sensing image fusion based on sparse representation

To improve the quality of the fused image, we propose a remote sensing image fusion method based on sparse representation. In the method, first, we represent the source images with sparse coefficients. Second, the larger values of sparse coefficients of panchromatic (Pan) image is set to 0. Third, t...

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Bibliographic Details
Main Authors: Xianchuan Yu, Guanyin Gao, Jindong Xu, Guian Wang
Format: Conference Proceeding
Language:English
Subjects:
Dictionaries
dictionary learning
Discrete wavelet transforms
Educational institutions
Image fusion
Principal component analysis
Remote sensing
sparse representation
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Summary:To improve the quality of the fused image, we propose a remote sensing image fusion method based on sparse representation. In the method, first, we represent the source images with sparse coefficients. Second, the larger values of sparse coefficients of panchromatic (Pan) image is set to 0. Third, the coefficients of panchromatic (Pan) and multispectral (MS) image are combined with the linear weighted averaging fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. The proposed method is compared with intensity-hue-saturation (IHS), Brovey transform (Brovey), discrete wavelet transform (DWT), principal component analysis (PCA) and fast discrete curvelet transform (FDCT) methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2014.6947072