Pansharpening algorithm of remote sensing images based on NSCT and PCNN

XU Xinyu; LI Xiaojun; ZHAO Heting; GAI Junfei

doi:10.6046/zrzyyg.2022159

2023 Vol. 35, No. 3

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Article navigation > Remote Sensing for Natural Resources > 2023 > 35(3): 64-70

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XU Xinyu, LI Xiaojun, ZHAO Heting, GAI Junfei. 2023. Pansharpening algorithm of remote sensing images based on NSCT and PCNN. Remote Sensing for Natural Resources, 35(3): 64-70. doi: 10.6046/zrzyyg.2022159

Citation:

XU Xinyu, LI Xiaojun, ZHAO Heting, GAI Junfei. 2023. Pansharpening algorithm of remote sensing images based on NSCT and PCNN. Remote Sensing for Natural Resources, 35(3): 64-70. doi: 10.6046/zrzyyg.2022159

Pansharpening algorithm of remote sensing images based on NSCT and PCNN

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

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Corresponding author: LI Xiaojun

Received Date: 22 April 2022

Revised Date: 05 September 2022

Publish Date: 19 September 2023

Abstract

Abstract

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.
- pulse coupled neural network /
- nonsubsampled Contourlet transform /
- remote sensing image fusion /
- multispectral remote sensing image