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Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced

Nhat-Duc Hoang1,2

  • 1Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.

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Summary
This summary is machine-generated.

This study developed advanced neural network models for accurate impervious surface detection using Sentinel-2 satellite data. The Nadam optimizer achieved the highest accuracy, aiding urban planning.

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Area of Science:

  • Remote Sensing
  • Urban Planning
  • Artificial Intelligence

Background:

  • Up-to-date impervious surface information is crucial for effective urban planning and management.
  • Accurate detection of impervious surfaces aids in understanding urban development and its environmental impact.

Purpose of the Study:

  • To develop and evaluate neural computing models for automatic impervious surface area detection at a regional scale.
  • To compare the performance of various advanced optimizers in training artificial neural networks for this task.

Main Methods:

  • Utilized Sentinel-2 satellite imagery of Da Nang city, Vietnam.
  • Employed advanced optimizers: Adam, Adamax, Nadam, AdamW, AMSGrad, benchmarked against GDM.
  • Incorporated texture descriptors and statistical measurements for feature extraction.

Main Results:

  • The Nadam optimizer-based neural network model demonstrated superior performance.
  • Achieved high classification accuracy (97.331%), precision (0.961), recall (0.984), NPV (0.985), and F1 score (0.972).
  • Statistical tests validated the model's predictive accuracy.

Conclusions:

  • The developed Nadam-optimized neural network model is effective for regional impervious surface detection.
  • This tool can significantly support urban land-use planning and management decisions.
  • The study highlights the importance of advanced optimizers in improving remote sensing data analysis for urban environments.