An SAR and optical image fusion algorithm coupling non-local self-similarity and divergence
FU Yukai1(), YANG Shuwen1,2,3(), YAN Heng1, XUE Qing1, HONG Weili1, SU Hang1
1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China 2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China 3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
Currently, the high-quality fusion of SAR and optical images is a hot research topic. However, the significant radiation difference and weak gray correlation between SAR and optical images greatly reduce the fusion quality. In this regard, this study proposed a SAR and optical remote sensing image fusion algorithm that coupled non-local self-similarity and divergence. First, images were decomposed in the frequency domain. Then, the non-local directional entropy and divergence were used as characteristic parameters to guide the fusion of low- and high-frequency components, respectively. Finally, the fusion components were reconstructed to obtain fusion images with clear structural features and rich spectral information. The comparative experiments verified the effectiveness of the proposed algorithm in fusing SAR with optical images and its superiority in maintaining structural features and reducing spectral distortion.
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FU Yukai, YANG Shuwen, YAN Heng, XUE Qing, HONG Weili, SU Hang. An SAR and optical image fusion algorithm coupling non-local self-similarity and divergence. Remote Sensing for Natural Resources, 2023, 35(1): 99-106.
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2023, Vol. 35 
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