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Related Experiment Video

Updated: Jun 28, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
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IL-HS: a deep inception-LSTM architecture for enhanced lithological mapping using EnMAP hyperspectral remote sensing

Younes Khandouch1,2, Soufiane Hajaj3,4, Abderrazak El Harti5

  • 1Laboratory of Metrology and Information Processing, Physics Department, Faculty of Sciences Agadir, Ibn Zohr University, B.P. 8106, 80000, Agadir, Morocco. khandouch.younes@edu.uiz.ac.ma.

Scientific Reports
|May 17, 2026
PubMed
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A new deep learning model, Inception-LSTM Hyperspectral Mapper (IL-HS), significantly improves lithological mapping accuracy using hyperspectral satellite data. This advanced framework enhances mineral exploration and geoscientific understanding in complex terrains.

Area of Science:

  • Geoscience
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Accurate lithological mapping is vital for geoscience and mineral exploration but challenging in complex, semi-arid regions.
  • Hyperspectral satellite imagery offers rich spectral information but requires sophisticated processing for effective lithological classification.

Purpose of the Study:

  • To introduce the Inception-LSTM Hyperspectral Mapper (IL-HS), a deep learning framework for enhanced lithological classification.
  • To evaluate the performance of IL-HS using EnMAP hyperspectral data in a geologically complex area.

Main Methods:

  • Developed a deep learning framework integrating InceptionV2 for spatial feature extraction and a bidirectional long short-term memory (Bi-LSTM) module for spectral information.
  • Applied the IL-HS model to EnMAP hyperspectral data from the Kerdous inlier, Anti-Atlas, Morocco.
Keywords:
Deep inception architectureDeep learningEnMAP satellite dataHyperspectral satellite imageryLithological mappingLong short-term memory (LSTM)

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  • Compared IL-HS performance against Support Vector Machines and 3D Convolutional Neural Networks.

    • Main Results:

    • Achieved an overall accuracy of 98.05% across 26 lithological units, significantly outperforming existing models.
    • Demonstrated perfect recall for copper and manganese formations and reliable distinction of spectrally similar units.
    • Effectively mitigated spectral redundancy and mixing artifacts in heterogeneous and altered terrains.

    Conclusions:

    • The Inception-LSTM Hyperspectral Mapper (IL-HS) is a robust and scalable approach for hyperspectral lithological classification and mapping.
    • IL-HS shows significant potential for geoscientific research, mineral resource assessment, and sustainable exploration.
    • Deep learning models offer powerful solutions for complex remote sensing data analysis in geology.