View in EDS HTML Full Text PDF Full Text

Spectral mastery: deep autoencoder driven classification across hyperspectral datasets.

Saved in:
Bibliographic Details
Title: Spectral mastery: deep autoencoder driven classification across hyperspectral datasets.
Authors: Das, Bhaskar1 (AUTHOR) bhaskar04.rs20@iiitranchi.ac.in, Bhakta, Dhananjoy1 (AUTHOR), Kumar, Lalan2 (AUTHOR)
Source: Journal of Earth System Science. Jun2026, Vol. 135 Issue 2, p1-10. 10p.
Subject Terms: *Autoencoders, *Optimizers (Computer software), *Image recognition (Computer vision), *Remote sensing, *Mathematical optimization, *Spectral imaging
Geographic Terms: Botswana
Abstract: Hyperspectral image (HSI) classification plays a pivotal role in remote sensing applications, providing precise identification of land cover types through detailed spectral analysis. The present study evaluates the effectiveness of deep autoencoder architectures across multiple hyperspectral datasets, including Pavia Centre, Pavia University, and Botswana. The autoencoder models were optimized and assessed using various optimizers – Adam, SGD, Adadelta, and Adagrad – over different epoch settings. Experimental results demonstrate exceptional classification accuracy, notably achieving up to 98.848% on Pavia Centre with the SGD optimizer, and 99.81% on the Botswana dataset with Adagrad, highlighting the method's broad applicability and robustness. Across datasets, SGD exhibited the most stable performance among the tested optimizers. The outcomes affirm that autoencoder-based deep learning approaches efficiently encode intricate spectral–spatial characteristics, substantially advancing accuracy and reliability in hyperspectral image analysis. Research highlights: Developed a hybrid HyperspectralAE model that integrates autoencoder-based reconstruction with supervised classification, achieving robust spectral–spatial feature learning. Achieved state-of-the-art accuracies across multiple hyperspectral datasets that outperform existing autoencoder-based approaches. Demonstrated that optimizer choice (SGD, Adagrad) plays a crucial role in model stability and performance, providing practical insights for hyperspectral deep learning applications. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 193197818
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Spectral mastery: deep autoencoder driven classification across hyperspectral datasets.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Das%2C+Bhaskar%22">Das, Bhaskar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bhaskar04.rs20@iiitranchi.ac.in</i><br /><searchLink fieldCode="AR" term="%22Bhakta%2C+Dhananjoy%22">Bhakta, Dhananjoy</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kumar%2C+Lalan%22">Kumar, Lalan</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Earth+System+Science%22">Journal of Earth System Science</searchLink>. Jun2026, Vol. 135 Issue 2, p1-10. 10p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Autoencoders%22">Autoencoders</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimizers+%28Computer+software%29%22">Optimizers (Computer software)</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br />*<searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Spectral+imaging%22">Spectral imaging</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Botswana%22">Botswana</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Hyperspectral image (HSI) classification plays a pivotal role in remote sensing applications, providing precise identification of land cover types through detailed spectral analysis. The present study evaluates the effectiveness of deep autoencoder architectures across multiple hyperspectral datasets, including Pavia Centre, Pavia University, and Botswana. The autoencoder models were optimized and assessed using various optimizers – Adam, SGD, Adadelta, and Adagrad – over different epoch settings. Experimental results demonstrate exceptional classification accuracy, notably achieving up to 98.848% on Pavia Centre with the SGD optimizer, and 99.81% on the Botswana dataset with Adagrad, highlighting the method's broad applicability and robustness. Across datasets, SGD exhibited the most stable performance among the tested optimizers. The outcomes affirm that autoencoder-based deep learning approaches efficiently encode intricate spectral–spatial characteristics, substantially advancing accuracy and reliability in hyperspectral image analysis. Research highlights: Developed a hybrid HyperspectralAE model that integrates autoencoder-based reconstruction with supervised classification, achieving robust spectral–spatial feature learning. Achieved state-of-the-art accuracies across multiple hyperspectral datasets that outperform existing autoencoder-based approaches. Demonstrated that optimizer choice (SGD, Adagrad) plays a crucial role in model stability and performance, providing practical insights for hyperspectral deep learning applications. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193197818
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s12040-026-02777-5
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 1
    Subjects:
      – SubjectFull: Autoencoders
        Type: general
      – SubjectFull: Optimizers (Computer software)
        Type: general
      – SubjectFull: Image recognition (Computer vision)
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Spectral imaging
        Type: general
      – SubjectFull: Botswana
        Type: general
    Titles:
      – TitleFull: Spectral mastery: deep autoencoder driven classification across hyperspectral datasets.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Das, Bhaskar
      – PersonEntity:
          Name:
            NameFull: Bhakta, Dhananjoy
      – PersonEntity:
          Name:
            NameFull: Kumar, Lalan
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 02534126
          Numbering:
            – Type: volume
              Value: 135
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of Earth System Science
              Type: main
ResultId 1