CDGP-Net: Channel-Decoupling and Geographic-Prior Fusion for Spatial Super-Resolution of HIRAS Radiances with Co-Platform MERSI-II.
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| Title: | CDGP-Net: Channel-Decoupling and Geographic-Prior Fusion for Spatial Super-Resolution of HIRAS Radiances with Co-Platform MERSI-II. |
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| Authors: | Yang, Zhiyu1,2 (AUTHOR), Hu, Yong1,2 (AUTHOR), Zeng, Changwen3 (AUTHOR), Gu, Mingjian1 (AUTHOR) gumingjian@mail.sitp.ac.cn |
| Source: | Remote Sensing. Aug2026, Vol. 18 Issue 15, p2575. 38p. |
| Subjects: | Multisensor data fusion, Spatial resolution, Coasts, Artificial neural networks, Radiative transfer, Numerical weather forecasting |
| Abstract: | Highlights: What are the main findings? CDGP-Net, an unsupervised fusion network, super-resolves FY-3D HIRAS infrared sounder radiances from 16 km to 4 km using co-platform MERSI-II imagery. Channel decoupling exploits physically related non-overlapping MERSI-II channel information, and a geographic prior maintains continuity across HIRAS field-of-view gaps while preserving spectral structure. What are the implications of the main findings? CDGP-Net recovers fine-scale structures most effectively in heterogeneous partial-cloud and coastal scenes, where high-resolution radiances are most needed. LBLRTM-based radiative-transfer references enable physics-based evaluation of reconstructed 4 km radiances without true high-resolution observations, providing a basis for exploring their potential value in convection-permitting NWP. Hyperspectral infrared sounders provide valuable observations for numerical weather prediction (NWP), but their native nadir spatial resolution of approximately 12–16 km is coarser than the approximately 4 km grid spacing commonly used in convection-permitting regional forecasting systems. To enhance the spatial resolution of these observations toward this scale, we propose the Channel-Decoupling and Geographic-Prior Fusion Network (CDGP-Net), an unsupervised hyperspectral–multispectral fusion framework that reconstructs 4 km high-spatial-resolution hyperspectral radiances by fusing the FengYun-3D (FY-3D) Hyperspectral Infrared Atmospheric Sounder (HIRAS) data with co-platform Medium Resolution Spectral Imager II (MERSI-II) imagery while preserving the original spectral sampling. To adapt hyperspectral–multispectral fusion to infrared sounder data, CDGP-Net incorporates two components: a self-reconstruction and spectral-degradation channel-decoupling (SDCD) design, which allows physically related non-overlapping MERSI-II infrared information to be used as an auxiliary input while keeping the spectral degradation physically consistent; and a reconstruction-domain geographic-prior regularization (RGPR) scheme, which constrains the reconstructed radiances in both geographic space and spectral shape. Because true high-resolution observations are unavailable, we further introduce a radiative-transfer-anchored evaluation (RTAE) scheme that uses the line-by-line radiative transfer model (LBLRTM) simulations driven by reanalysis and forecast atmospheric fields as independent physical references. For the selected FY-3D overpass cases, the evaluation using Ref-HR as the high-resolution physical reference shows that CDGP-Net improves the peak signal-to-noise ratio (PSNR) by 3.8 dB and reduces the spectral angle mapper (SAM) and erreur relative globale adimensionnelle de synthèse (ERGAS) by 64.1% and 54.6%, respectively, compared with the unmixing baseline. Under the same evaluation conditions, relative to geographic interpolation, it improves the structural similarity index measure (SSIM) by 16.4% and reduces ERGAS by 8.8%, with the clearest advantages in partial-cloud and coastal transition scenes. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI 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.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 196272255 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: CDGP-Net: Channel-Decoupling and Geographic-Prior Fusion for Spatial Super-Resolution of HIRAS Radiances with Co-Platform MERSI-II. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Zhiyu%22">Yang, Zhiyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Yong%22">Hu, Yong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zeng%2C+Changwen%22">Zeng, Changwen</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gu%2C+Mingjian%22">Gu, Mingjian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gumingjian@mail.sitp.ac.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Aug2026, Vol. 18 Issue 15, p2575. 38p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+resolution%22">Spatial resolution</searchLink><br /><searchLink fieldCode="DE" term="%22Coasts%22">Coasts</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Radiative+transfer%22">Radiative transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+weather+forecasting%22">Numerical weather forecasting</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? CDGP-Net, an unsupervised fusion network, super-resolves FY-3D HIRAS infrared sounder radiances from 16 km to 4 km using co-platform MERSI-II imagery. Channel decoupling exploits physically related non-overlapping MERSI-II channel information, and a geographic prior maintains continuity across HIRAS field-of-view gaps while preserving spectral structure. What are the implications of the main findings? CDGP-Net recovers fine-scale structures most effectively in heterogeneous partial-cloud and coastal scenes, where high-resolution radiances are most needed. LBLRTM-based radiative-transfer references enable physics-based evaluation of reconstructed 4 km radiances without true high-resolution observations, providing a basis for exploring their potential value in convection-permitting NWP. Hyperspectral infrared sounders provide valuable observations for numerical weather prediction (NWP), but their native nadir spatial resolution of approximately 12–16 km is coarser than the approximately 4 km grid spacing commonly used in convection-permitting regional forecasting systems. To enhance the spatial resolution of these observations toward this scale, we propose the Channel-Decoupling and Geographic-Prior Fusion Network (CDGP-Net), an unsupervised hyperspectral–multispectral fusion framework that reconstructs 4 km high-spatial-resolution hyperspectral radiances by fusing the FengYun-3D (FY-3D) Hyperspectral Infrared Atmospheric Sounder (HIRAS) data with co-platform Medium Resolution Spectral Imager II (MERSI-II) imagery while preserving the original spectral sampling. To adapt hyperspectral–multispectral fusion to infrared sounder data, CDGP-Net incorporates two components: a self-reconstruction and spectral-degradation channel-decoupling (SDCD) design, which allows physically related non-overlapping MERSI-II infrared information to be used as an auxiliary input while keeping the spectral degradation physically consistent; and a reconstruction-domain geographic-prior regularization (RGPR) scheme, which constrains the reconstructed radiances in both geographic space and spectral shape. Because true high-resolution observations are unavailable, we further introduce a radiative-transfer-anchored evaluation (RTAE) scheme that uses the line-by-line radiative transfer model (LBLRTM) simulations driven by reanalysis and forecast atmospheric fields as independent physical references. For the selected FY-3D overpass cases, the evaluation using Ref-HR as the high-resolution physical reference shows that CDGP-Net improves the peak signal-to-noise ratio (PSNR) by 3.8 dB and reduces the spectral angle mapper (SAM) and erreur relative globale adimensionnelle de synthèse (ERGAS) by 64.1% and 54.6%, respectively, compared with the unmixing baseline. Under the same evaluation conditions, relative to geographic interpolation, it improves the structural similarity index measure (SSIM) by 16.4% and reduces ERGAS by 8.8%, with the clearest advantages in partial-cloud and coastal transition scenes. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI 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: 10.3390/rs18152575 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 38 StartPage: 2575 Subjects: – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Spatial resolution Type: general – SubjectFull: Coasts Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Radiative transfer Type: general – SubjectFull: Numerical weather forecasting Type: general Titles: – TitleFull: CDGP-Net: Channel-Decoupling and Geographic-Prior Fusion for Spatial Super-Resolution of HIRAS Radiances with Co-Platform MERSI-II. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Zhiyu – PersonEntity: Name: NameFull: Hu, Yong – PersonEntity: Name: NameFull: Zeng, Changwen – PersonEntity: Name: NameFull: Gu, Mingjian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 15 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
