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Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation

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  • 1School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing, China.

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This study introduces a deep learning model using Backpropagation Neural Network (BPNN) to accurately extract lithological information from hyperspectral remote sensing data. The enhanced BPNN model significantly improves rock classification accuracy and Kappa coefficient compared to traditional methods.

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Area of Science:

  • Geoscience
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Hyperspectral remote sensing data presents isomorphism challenges in lithological information extraction.
  • Accurate classification of rock types is crucial for geological surveys and resource exploration.

Purpose of the Study:

  • To address isomorphism issues in hyperspectral data processing.
  • To enhance the accuracy of lithological information extraction and classification.
  • To develop a deep learning model for improved rock identification.

Main Methods:

  • Utilized Backpropagation Neural Network (BPNN) for hyperspectral data analysis.
  • Normalized hyperspectral image data.
  • Employed lithological spectral and spatial information for feature extraction.
  • Constructed a deep learning-based model for lithological information extraction.

Main Results:

  • The deep learning model achieved an overall accuracy of 90.58% and a Kappa coefficient of 0.8676.
  • The proposed BPNN model demonstrated improved recognition accuracy (8.5% increase) and Kappa coefficient (0.12 increase) compared to traditional BPNN.
  • The model precisely distinguishes rock mass properties, outperforming other analysis models.

Conclusions:

  • The developed deep learning model offers significant research value and practical significance for hyperspectral rock and mineral classification.
  • The model effectively overcomes isomorphism challenges and enhances classification accuracy.
  • This approach provides a robust method for geological feature identification using remote sensing data.