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Segmentation on remote sensing imagery for atmospheric air pollution using divergent differential evolution

Meera Ramadas1, Ajith Abraham1

  • 1Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, WA 98071 USA.

Neural Computing & Applications
|October 24, 2022
PubMed
Summary
This summary is machine-generated.

A new Divergent Differential Evolution Algorithm (DiDE) improves air quality monitoring by enhancing satellite image segmentation. This novel approach significantly reduces computational delay and boosts image quality for better atmospheric analysis.

Keywords:
Air qualityEntropyMutationOMITEMISThresholding

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Area of Science:

  • Environmental Science
  • Computer Science
  • Remote Sensing

Background:

  • Air pollution poses global health risks, necessitating effective monitoring strategies.
  • Satellite remote sensing offers a powerful tool for global atmospheric monitoring.
  • Analyzing complex satellite radar images with subtle wavelength differences is challenging.

Purpose of the Study:

  • To develop an improved evolutionary technique for segmenting complex satellite remote sensing images.
  • To enhance the performance of Differential Evolution (DE) for image analysis applications.
  • To apply a novel algorithm to air quality monitoring using satellite imagery.

Main Methods:

  • A new variant of Differential Evolution, termed Divergent Differential Evolution (DiDE), was developed.
  • DiDE was benchmarked against traditional DE using fifteen functions.
  • The DiDE algorithm was integrated with Fuzzy Tsallis entropy for multi-level thresholding in image segmentation.

Main Results:

  • DiDE demonstrated superior performance compared to traditional DE algorithms in benchmark tests.
  • Application to TEMIS satellite imagery resulted in significantly improved image segmentation.
  • The DiDE-based approach led to profound improvements in image quality and reduced computational delay.

Conclusions:

  • The proposed DiDE algorithm offers a significant advancement for image segmentation tasks.
  • Integrating DiDE with Fuzzy Tsallis entropy enhances air quality monitoring through improved satellite image analysis.
  • This novel method provides a more efficient and effective solution for processing complex remote sensing data.