HPN-ICE: Information Cross Embedding for Hyperspectral Pansharpening.

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Bibliographic Details
Title:	HPN-ICE: Information Cross Embedding for Hyperspectral Pansharpening.
Authors:	JIN, Yan¹ jinyan@tiangong.edu.cn
Source:	Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p1029-1039. 11p.
Subjects:	Image fusion, Spatial resolution, Image enhancement (Imaging systems)
Abstract:	Hyperspectral (HS) pansharpening aims to generate high-spatial-resolution hyperspectral (HRHS) images by fusing panchromatic (PAN) images with low-spatial-resolution hyperspectral (LRHS) images. However, many existing HS pansharpening methods fail to capture global dependencies between cross-modal features, leading to spectral and spatial distortions.To address this issue, we propose a hyperspectral pansharpening network based on information cross embedding (HPN-ICE). The model progressively fuses HS and PAN image features through two modules: the global feature fusion module (GFFM) and the multi-directional feature enhancement module (MFEM). In GFFM, a feature embedding fusion module (FEFM) is firstly designed based on the information cross embedding, which efficiently fuses spectral and spatial features by establishing cross dependencies between two modal features. Then, a frequency-domain channel attention module (FCAM) is constructed to enhance the global spectral information in the frequency domain. MFEM is constructed to enhance the local details of fused features in multi-dimensional directions. Extensive experiments conducted on three widely used datasets demonstrate that HPN-ICE achieves significant improvements in both spatial and spectral quality metrics over some state-of-the-art (SOTA) methods. The code will be released on GitHub. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Hyperspectral (HS) pansharpening aims to generate high-spatial-resolution hyperspectral (HRHS) images by fusing panchromatic (PAN) images with low-spatial-resolution hyperspectral (LRHS) images. However, many existing HS pansharpening methods fail to capture global dependencies between cross-modal features, leading to spectral and spatial distortions.To address this issue, we propose a hyperspectral pansharpening network based on information cross embedding (HPN-ICE). The model progressively fuses HS and PAN image features through two modules: the global feature fusion module (GFFM) and the multi-directional feature enhancement module (MFEM). In GFFM, a feature embedding fusion module (FEFM) is firstly designed based on the information cross embedding, which efficiently fuses spectral and spatial features by establishing cross dependencies between two modal features. Then, a frequency-domain channel attention module (FCAM) is constructed to enhance the global spectral information in the frequency domain. MFEM is constructed to enhance the local details of fused features in multi-dimensional directions. Extensive experiments conducted on three widely used datasets demonstrate that HPN-ICE achieves significant improvements in both spatial and spectral quality metrics over some state-of-the-art (SOTA) methods. The code will be released on GitHub. [ABSTRACT FROM AUTHOR]
ISSN:	13303651
DOI:	10.17559/TV-20250706002800