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Entropy Estimators in SAR Image Classification.

Julia Cassetti1, Daiana Delgadino2, Andrea Rey3

  • 1Instituto del Desarrollo Humano, Universidad Nacional de General Sarmiento, Los Polvorines B1613, Provincia de Buenos Aires, Argentina.

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Summary
This summary is machine-generated.

This study explores using Shannon entropy estimators with Synthetic Aperture Radar (SAR) data for better environmental monitoring and disaster detection. The research evaluates different entropy estimation methods for improved image classification.

Keywords:
Shannon entropy estimatorclassificationfeature extractionsynthetic aperture radar

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Area of Science:

  • Earth Observation
  • Signal Processing
  • Information Theory

Background:

  • Remotely sensed data are crucial for environmental dynamics, forecasting, and disaster detection.
  • Microwave remote sensing, particularly Synthetic Aperture Radar (SAR), offers advantages over optical sensors due to its atmospheric resilience and active illumination.
  • SAR images present challenges like speckle noise, necessitating advanced modeling and information extraction techniques.

Purpose of the Study:

  • To evaluate the performance of various Shannon entropy estimators for SAR image analysis.
  • To investigate the utility of entropy as a feature for supervised and unsupervised classification of SAR data.
  • To propose and validate a methodology for optimizing non-parametric entropy estimators for SAR data.

Main Methods:

  • Utilized the G0 distribution family as a suitable model for SAR intensity data, accounting for textural variations.
  • Applied parametric and non-parametric Shannon entropy estimators to SAR images.
  • Implemented supervised and unsupervised classification algorithms using entropy features.
  • Developed and tested a fine-tuning methodology for non-parametric entropy estimators.

Main Results:

  • Demonstrated the effectiveness of Shannon entropy estimators as input features for SAR image classification.
  • Identified optimal entropy estimation techniques for different SAR data characteristics.
  • Successfully applied the developed methodology to real-world SAR datasets, showing improved classification performance.

Conclusions:

  • Shannon entropy is a valuable feature for extracting information from SAR data.
  • The proposed fine-tuning methodology enhances the performance of non-parametric entropy estimators for SAR analysis.
  • These techniques contribute to more accurate environmental monitoring and disaster early detection using SAR imagery.