An ICM-based adaptive pansharpening algorithm for hyperspectral images

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.