HPN-ICE: Information Cross Embedding for Hyperspectral Pansharpening.
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| 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] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195131790 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: HPN-ICE: Information Cross Embedding for Hyperspectral Pansharpening. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22JIN%2C+Yan%22">JIN, Yan</searchLink><relatesTo>1</relatesTo><i> jinyan@tiangong.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2026, Vol. 33 Issue 3, p1029-1039. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+fusion%22">Image fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+resolution%22">Spatial resolution</searchLink><br /><searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17559/TV-20250706002800 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1029 Subjects: – SubjectFull: Image fusion Type: general – SubjectFull: Spatial resolution Type: general – SubjectFull: Image enhancement (Imaging systems) Type: general Titles: – TitleFull: HPN-ICE: Information Cross Embedding for Hyperspectral Pansharpening. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: JIN, Yan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 33 – Type: issue Value: 3 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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