A multispectral image pansharpening algorithm based on nonsubsampled contourlet transform (NSCT) combined with a guided filter
XU Xinyu1(), LI Xiaojun1,2,3(), GE Junfei1, LI Yikun1,2,3
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
Remote sensing image fusion technology can combine and enhance information from two or more multi-source remote sensing images, making the fused image more accurate and comprehensive. The nonsubsampled contourlet transform (NSCT) is effective in extracting details from high-resolution remote sensing images through multi-scale and multi-directional decomposition, thus achieving image sharpening with high spatial resolution. However, traditional NSCT produces limited high-frequency details and is prone to introduce artifacts such as “ghosting” in fused images. To address this issue, the study proposed a new panchromatic sharpening fusion algorithm for remote sensing images by combining NSCT with a guided filter (GF). Specifically, the promoted algorithm extracted the detail components from histogram-matched images using the multi-scale, multi-direction decomposition and reconstruction properties of the NSCT. Meanwhile, it extracted multi-spectral detail components with panchromatic detail features using GF. Finally, the fused images with high-spatial and high-spectral resolutions were obtained by sharpening based on weighted detail components. The proposed algorithm was proved effective through both subjective and objective evaluations using multiple high-resolution remote sensing datasets.
徐欣钰, 李小军, 盖钧飞, 李轶鲲. 结合NSCT变换和引导滤波的多光谱图像全色锐化算法[J]. 自然资源遥感, 2025, 37(1): 24-30.
XU Xinyu, LI Xiaojun, GE Junfei, LI Yikun. A multispectral image pansharpening algorithm based on nonsubsampled contourlet transform (NSCT) combined with a guided filter. Remote Sensing for Natural Resources, 2025, 37(1): 24-30.
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2025, Vol. 37 
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