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Lithological Mapping in Plateau Regions by Integrating Spectral Feature Selection and Deep Learning: A Case Study of the Gonjo Area, Tibet.

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Title: Lithological Mapping in Plateau Regions by Integrating Spectral Feature Selection and Deep Learning: A Case Study of the Gonjo Area, Tibet.
Authors: Liu, Hanhu1 (AUTHOR), Huang, Xueliang1,2 (AUTHOR) 2025021044@stu.cdut.edu.cn, Wang, Wei1,2 (AUTHOR)
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1621. 30p.
Subjects: Hyperspectral imaging systems, Deep learning, Feature selection, Physiographic provinces, Remote sensing, Prospecting, Lithofacies
Geographic Terms: Tibetan Plateau, Tibet (China)
Abstract: Highlights: What are the main findings? This study systematically evaluates the impact of spectral feature sets (FB, SWIR, and FS), demonstrating that full-band data consistently achieves superior accuracy and identifies the SWIR region as the primary carrier of critical diagnostic spectral information due to its capture of Al–OH (2.20 µm), Mg–OH/Fe–OH (2.30–2.35 µm) and carbonate (2.35–2.50 µm) absorption features typical of the Gonjo area. This study develops a comparative framework among SVM, LSTM, MSCNN, and SSUN, demonstrating that the SSUN model achieves the highest accuracy among other evaluated algorithms in plateau lithological classification. What are the implications of the main findings? This study systematically applies Chinese GF-5 AHSI data for the first time to lithological classification in Gonjo County of the Qinghai–Tibet Plateau, providing empirical evidence for the extended application of Chinese satellites in geological mapping. The optimized classification results of the Spatial-Spectral joint model (SSUN) provide high-precision and high-reliability lithologic maps for regional geological surveys and mineral resource exploration in Gonjo County, Tibet. This study uses Gonjo County, Chamdo City, Tibet, as the study area and addresses the challenges of lithological complexity and low efficiency of conventional geological surveys in the Qinghai–Tibet Plateau. This study applies the first systematic application of Chinese GF-5 AHSI data to conduct detailed lithological classification in a plateau environment. Three types of datasets were constructed, including the full-band (FB) dataset, shortwave infrared diagnostic bands (SWIR), and feature-selected bands (FS). Four classification models—Support Vector Machine (SVM), Long Short-Term Memory network (LSTM), Multi-Scale Convolutional Neural Network (MSCNN), and Spectral-Spatial Unified Network (SSUN)—were comparatively evaluated to systematically assess the performance of spectral feature selection and deep learning methods for hyperspectral lithological classification. The experimental results explicitly demonstrate the superiority of spectral-spatial feature extraction. Specifically, compared to the baseline Support Vector Machine (SVM) model, which achieved an overall accuracy of 74.67% and a kappa coefficient of 0.6952, the proposed SSUN model demonstrated an advantage, reaching an overall accuracy of 90.94% and a kappa coefficient of 0.8917. By jointly extracting spectral sequence features and spatial contextual information, SSUN effectively suppresses noise and enhances the spatial continuity of lithological boundaries. The results demonstrate the high practical applicability and spectral fidelity of GF-5 AHSI data for lithological identification in plateau stratigraphic environments. The shortwave infrared region is confirmed to be a critical spectral domain for lithological discrimination, and spectral-spatial deep learning models can maintain high classification accuracy after feature dimensionality reduction, achieving a balance between classification efficiency and accuracy. This study provides reliable methodological support for remote sensing lithological mapping and mineral resource exploration in complex plateau geological environments. [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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  Data: Lithological Mapping in Plateau Regions by Integrating Spectral Feature Selection and Deep Learning: A Case Study of the Gonjo Area, Tibet.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Hanhu%22">Liu, Hanhu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Xueliang%22">Huang, Xueliang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> 2025021044@stu.cdut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Wei%22">Wang, Wei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1621. 30p.
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  Data: <searchLink fieldCode="DE" term="%22Hyperspectral+imaging+systems%22">Hyperspectral imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Physiographic+provinces%22">Physiographic provinces</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Prospecting%22">Prospecting</searchLink><br /><searchLink fieldCode="DE" term="%22Lithofacies%22">Lithofacies</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Tibetan+Plateau%22">Tibetan Plateau</searchLink><br /><searchLink fieldCode="DE" term="%22Tibet+%28China%29%22">Tibet (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? This study systematically evaluates the impact of spectral feature sets (FB, SWIR, and FS), demonstrating that full-band data consistently achieves superior accuracy and identifies the SWIR region as the primary carrier of critical diagnostic spectral information due to its capture of Al–OH (2.20 µm), Mg–OH/Fe–OH (2.30–2.35 µm) and carbonate (2.35–2.50 µm) absorption features typical of the Gonjo area. This study develops a comparative framework among SVM, LSTM, MSCNN, and SSUN, demonstrating that the SSUN model achieves the highest accuracy among other evaluated algorithms in plateau lithological classification. What are the implications of the main findings? This study systematically applies Chinese GF-5 AHSI data for the first time to lithological classification in Gonjo County of the Qinghai–Tibet Plateau, providing empirical evidence for the extended application of Chinese satellites in geological mapping. The optimized classification results of the Spatial-Spectral joint model (SSUN) provide high-precision and high-reliability lithologic maps for regional geological surveys and mineral resource exploration in Gonjo County, Tibet. This study uses Gonjo County, Chamdo City, Tibet, as the study area and addresses the challenges of lithological complexity and low efficiency of conventional geological surveys in the Qinghai–Tibet Plateau. This study applies the first systematic application of Chinese GF-5 AHSI data to conduct detailed lithological classification in a plateau environment. Three types of datasets were constructed, including the full-band (FB) dataset, shortwave infrared diagnostic bands (SWIR), and feature-selected bands (FS). Four classification models—Support Vector Machine (SVM), Long Short-Term Memory network (LSTM), Multi-Scale Convolutional Neural Network (MSCNN), and Spectral-Spatial Unified Network (SSUN)—were comparatively evaluated to systematically assess the performance of spectral feature selection and deep learning methods for hyperspectral lithological classification. The experimental results explicitly demonstrate the superiority of spectral-spatial feature extraction. Specifically, compared to the baseline Support Vector Machine (SVM) model, which achieved an overall accuracy of 74.67% and a kappa coefficient of 0.6952, the proposed SSUN model demonstrated an advantage, reaching an overall accuracy of 90.94% and a kappa coefficient of 0.8917. By jointly extracting spectral sequence features and spatial contextual information, SSUN effectively suppresses noise and enhances the spatial continuity of lithological boundaries. The results demonstrate the high practical applicability and spectral fidelity of GF-5 AHSI data for lithological identification in plateau stratigraphic environments. The shortwave infrared region is confirmed to be a critical spectral domain for lithological discrimination, and spectral-spatial deep learning models can maintain high classification accuracy after feature dimensionality reduction, achieving a balance between classification efficiency and accuracy. This study provides reliable methodological support for remote sensing lithological mapping and mineral resource exploration in complex plateau geological environments. [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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        Value: 10.3390/rs18101621
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 30
        StartPage: 1621
    Subjects:
      – SubjectFull: Hyperspectral imaging systems
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Physiographic provinces
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Prospecting
        Type: general
      – SubjectFull: Lithofacies
        Type: general
      – SubjectFull: Tibetan Plateau
        Type: general
      – SubjectFull: Tibet (China)
        Type: general
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      – TitleFull: Lithological Mapping in Plateau Regions by Integrating Spectral Feature Selection and Deep Learning: A Case Study of the Gonjo Area, Tibet.
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            NameFull: Liu, Hanhu
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            NameFull: Huang, Xueliang
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              M: 05
              Text: May2026
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              Y: 2026
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