View in EDS

Boosting hyperspectral image classification with Gate-Shift-Fuse mechanisms in a novel CNN-Transformer approach.

Saved in:
Bibliographic Details
Title: Boosting hyperspectral image classification with Gate-Shift-Fuse mechanisms in a novel CNN-Transformer approach.
Authors: Guerri, Mohamed Fadhlallah1,2 (AUTHOR) mohamedfadhlallah.guerri@unisalento.it, Distante, Cosimo1,2 (AUTHOR) cosimo.distante@cnr.it, Spagnolo, Paolo2 (AUTHOR) paolo.spagnolo@cnr.it, Taleb-Ahmed, Abdelmalik3 (AUTHOR) Abdelmalik.Taleb-Ahmed@uphf.fr
Source: Computers & Electronics in Agriculture. Oct2025:Part A, Vol. 237, pN.PAG-N.PAG. 1p.
Subjects: Transformer models, Feature extraction, Statistical reliability, Remote-sensing images, Image recognition (Computer vision)
Abstract: Hyperspectral Image (HSI) classification categorizes every pixel sample into distinct land-cover types. While CNN-based methods have demonstrated significant advances in feature representation, they often struggle with extracting deep features. On the other hand, transformer models excel in capturing high-level semantic features, offering complementary strengths. This paper introduces a novel HSI classification framework that combines convolutional blocks with Gate-Shift-Fuse (GSF) and transformer blocks to leverage CNNs for local feature extraction and transformers for long-range context modelling. The GSF block enhances the integration of spatial–spectral features, while an effective attention mechanism module refines feature extraction from HSI cubes. The proposed method was evaluated on four benchmark datasets (Indian Pines, Pavia University, WHU-Hi-LongKou, and WHU-Hi-HanChuan). It achieved state-of-the-art overall accuracies (OA) of 99.47% on the Indian Pines dataset and 99.86% on WHU-Hi-LongKou, demonstrating significant improvements over existing models. These results highlight the framework's ability to address challenges like spectral variability and unbalanced datasets. Our contributions include the integration of the GSF block for enhanced spatial–spectral feature fusion and the application of transformers for robust high-level semantic understanding, setting a new benchmark in HSI classification. • We introduced the Gate-Shift-Fuse (GSF) designed specifically for HSI classification. • GSF strengthen CNNs and transformers in extracting local and global features. • A local-global attention mechanism module for HSI cubes is proposed. • long-range context modelling leveraged by strengths of CNNs and transformers. • Superior classification performances achieved on well known HSI benchmark datasets. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Electronics in Agriculture is the property of Elsevier B.V. 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 Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 187230595
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Boosting hyperspectral image classification with Gate-Shift-Fuse mechanisms in a novel CNN-Transformer approach.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Guerri%2C+Mohamed+Fadhlallah%22">Guerri, Mohamed Fadhlallah</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> mohamedfadhlallah.guerri@unisalento.it</i><br /><searchLink fieldCode="AR" term="%22Distante%2C+Cosimo%22">Distante, Cosimo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> cosimo.distante@cnr.it</i><br /><searchLink fieldCode="AR" term="%22Spagnolo%2C+Paolo%22">Spagnolo, Paolo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> paolo.spagnolo@cnr.it</i><br /><searchLink fieldCode="AR" term="%22Taleb-Ahmed%2C+Abdelmalik%22">Taleb-Ahmed, Abdelmalik</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> Abdelmalik.Taleb-Ahmed@uphf.fr</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Computers+%26+Electronics+in+Agriculture%22">Computers & Electronics in Agriculture</searchLink>. Oct2025:Part A, Vol. 237, pN.PAG-N.PAG. 1p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+reliability%22">Statistical reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Hyperspectral Image (HSI) classification categorizes every pixel sample into distinct land-cover types. While CNN-based methods have demonstrated significant advances in feature representation, they often struggle with extracting deep features. On the other hand, transformer models excel in capturing high-level semantic features, offering complementary strengths. This paper introduces a novel HSI classification framework that combines convolutional blocks with Gate-Shift-Fuse (GSF) and transformer blocks to leverage CNNs for local feature extraction and transformers for long-range context modelling. The GSF block enhances the integration of spatial–spectral features, while an effective attention mechanism module refines feature extraction from HSI cubes. The proposed method was evaluated on four benchmark datasets (Indian Pines, Pavia University, WHU-Hi-LongKou, and WHU-Hi-HanChuan). It achieved state-of-the-art overall accuracies (OA) of 99.47% on the Indian Pines dataset and 99.86% on WHU-Hi-LongKou, demonstrating significant improvements over existing models. These results highlight the framework's ability to address challenges like spectral variability and unbalanced datasets. Our contributions include the integration of the GSF block for enhanced spatial–spectral feature fusion and the application of transformers for robust high-level semantic understanding, setting a new benchmark in HSI classification. • We introduced the Gate-Shift-Fuse (GSF) designed specifically for HSI classification. • GSF strengthen CNNs and transformers in extracting local and global features. • A local-global attention mechanism module for HSI cubes is proposed. • long-range context modelling leveraged by strengths of CNNs and transformers. • Superior classification performances achieved on well known HSI benchmark datasets. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computers & Electronics in Agriculture is the property of Elsevier B.V. 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=187230595
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.compag.2025.110489
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Transformer models
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Statistical reliability
        Type: general
      – SubjectFull: Remote-sensing images
        Type: general
      – SubjectFull: Image recognition (Computer vision)
        Type: general
    Titles:
      – TitleFull: Boosting hyperspectral image classification with Gate-Shift-Fuse mechanisms in a novel CNN-Transformer approach.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Guerri, Mohamed Fadhlallah
      – PersonEntity:
          Name:
            NameFull: Distante, Cosimo
      – PersonEntity:
          Name:
            NameFull: Spagnolo, Paolo
      – PersonEntity:
          Name:
            NameFull: Taleb-Ahmed, Abdelmalik
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Text: Oct2025:Part A
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 01681699
          Numbering:
            – Type: volume
              Value: 237
          Titles:
            – TitleFull: Computers & Electronics in Agriculture
              Type: main
ResultId 1