Original scientific paper

https://doi.org/10.17559/TV-20250706002800

HPN-ICE: Information Cross Embedding for Hyperspectral Pansharpening

Yan Jin ; Tiangong University, School of Computer Science and Technology, Tianjin 300387, China *

* Corresponding author.

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.

Keywords

frequency-domain; hyperspectral pansharpening; mamba model; multi-directional feature

Hrčak ID:

346714

URI

https://hrcak.srce.hr/346714

Publication date:

30.4.2026.

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