Pansharpening algorithm of remote sensing images based on NSCT and PCNN
XU Xinyu1(), LI Xiaojun1,2,3(), ZHAO Heting1, GAI Junfei1
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
Conventional pansharpening fusion methods suffer inaccurate extraction of details and low spectrum fusion accuracy. This study proposed a pansharpening algorithm of remote sensing images based on nonsubsampled Contourlet transform (NSCT) and pulse coupled neural networks (PCNN) by combining the multi-scale and -directional decomposition characteristics of NSCT and the pulse synchronous emission characteristics of PCNN. The process of this pansharpening algorithm is as follows: first, the details of panchromatic images were extracted through NSCT; then, the extracted detail features were injected into the irregular segmentation regions obtained using the PCNN model; finally, the sharpening fusion results of high-resolution multispectral remote-sensing images were obtained through statistical weighting. As corroborated by the experimental results of WorldView-2 and GF-2 data sets, the pansharpening algorithm outperforms other remote sensing image fusion algorithms in detail preservation and spectral consistency, verifying its effectiveness.
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2023, Vol. 35 
), 李小军1,2,3(
