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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (2) : 97-104     DOI: 10.6046/zrzyyg.2023026
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An ICM-based adaptive pansharpening algorithm for hyperspectral images
ZHAO Heting1(), LI Xiaojun1,2,3(), XU Xinyu1, 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
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Abstract  

Considering spectral distortion and insufficient texture details in the pansharpening of hyperspectral images, this study proposed an adaptive pansharpening algorithm for hyperspectral images based on the intersecting cortical model (ICM) for image segmentation. First, hyperspectral images were matched and fused with multispectral images with similar spatial resolution. Then, the matching and fusion results were fused with high-resolution panchromatic images, obtaining the fusion results possessing both the high spatial resolution of panchromatic images and the spectral resolution of hyperspectral images. Moreover, the grey wolf optimizer (GWO) was employed in sharpening fusion to adaptively optimize ICM parameters, generating the optimal irregular segmentation regions, thus providing more accurate and comprehensive details and spectral information for hyperspectral images. Finally, experiments were conducted on the proposed algorithm using two hyperspectral datasets from the ZY-1 02D satellite. The experimental results demonstrate that the proposed algorithm manifested the optimal performance in the evaluation indices of spatial details and spectral information, substantiating its effectiveness.
Keywords pansharpening      intersecting cortical model      hyperspectral image      grey wolf optimizer      remote sensing image fusion     
ZTFLH:  TP79  
Issue Date: 14 June 2024
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Heting ZHAO
Xiaojun LI
Xinyu XU
Junfei GAI
Cite this article:   
Heting ZHAO,Xiaojun LI,Xinyu XU, et al. An ICM-based adaptive pansharpening algorithm for hyperspectral images[J]. Remote Sensing for Natural Resources, 2024, 36(2): 97-104.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023026     OR     https://www.gtzyyg.com/EN/Y2024/V36/I2/97
Fig.1  Neural model of ICM
Fig.2  Schematic diagram of algorithm flow
Fig.3  Process of ICM parameter optimization
Fig.4  Pansharpening results of Liujiaxia mountain dataset
Fig.5  Pansharpening results of Lanzhou City dataset
方法 RMSE PSNR/dB ERGAS SSIM DD SAM/(°)
本文方法 20.009 7 30.177 6 0.451 4 0.608 8 8.602 3 2.291 6
GS 32.571 1 21.565 7 0.815 6 0.577 1 26.195 0 2.433 0
PCA 32.249 5 21.650 8 0.810 0 0.579 5 25.912 7 2.413 0
Brovey 34.867 1 20.970 2 0.854 4 0.568 3 28.263 1 2.412 2
IHS 40.792 3 18.978 6 1.054 0 0.516 4 33.807 7 2.864 0
ATWT 22.164 7 24.803 8 0.626 4 0.587 3 17.033 3 2.635 9
HPF 23.479 3 24.319 2 0.652 9 0.569 7 18.144 1 2.293 1
SFIM 22.284 9 24.761 2 0.629 2 0.572 8 17.070 9 2.295 1
BDSD-PC 29.697 2 22.400 5 1.502 4 0.484 9 22.725 8 4.139 5
Tab.1  Comparison results of Liujiaxia mountain datasets
方法 RMSE PSNR/dB ERGAS SSIM DD SAM/(°)
本文方法 49.055 6 19.191 1 1.717 0 0.599 7 35.245 5 4.780 3
GS 65.213 0 16.764 2 1.880 6 0.459 9 52.089 3 4.972 8
PCA 64.608 2 16.845 4 1.875 0 0.462 5 51.600 5 4.948 4
Brovey 57.400 8 17.895 1 1.799 8 0.496 3 45.718 0 4.934 3
IHS 72.438 5 15.347 1 2.099 2 0.426 6 57.988 8 5.358 0
ATWT 55.754 9 18.100 8 1.793 8 0.472 8 44.263 7 5.280 2
HPF 57.058 7 17.903 9 1.807 8 0.465 9 45.314 7 4.901 8
SFIM 56.417 8 18.001 4 1.801 0 0.468 3 44.791 7 4.913 1
BDSD-PC 50.277 3 18.821 6 1.765 3 0.455 9 39.718 2 5.437 6
Tab.2  Comparison results of Lanzhou City datasets
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