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Relaxation-Based Radiometric Normalization for Multitemporal Cross-Sensor Satellite Images.

Gabriel Yedaya Immanuel Ryadi1, Muhammad Aldila Syariz1,2, Chao-Hung Lin1

  • 1Department of Geomatics, National Cheng Kung University, Tainan City 70101, Taiwan.

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Summary
This summary is machine-generated.

A new relaxation-based algorithm enhances satellite image normalization for better Earth surface monitoring. This method improves radiometric consistency and preserves features across different sensors and times.

Keywords:
IR-MADimage normalizationmultitemporal cross-sensor imagerelaxationvisual consistency

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Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Image Processing

Background:

  • Multitemporal cross-sensor imagery is crucial for Earth surface monitoring.
  • Inconsistent atmospheric and surface conditions cause visual disparities in satellite images, hindering analysis.
  • Existing normalization methods like histogram matching and iteratively reweighted multivariate alteration detection (IR-MAD) have limitations in feature preservation and reference image dependency.

Purpose of the Study:

  • To introduce a novel relaxation-based algorithm for satellite image normalization.
  • To overcome the limitations of existing methods in maintaining image features and reducing reliance on reference images.
  • To improve radiometric consistency and accuracy in multitemporal cross-sensor satellite imagery.

Main Methods:

  • A relaxation-based algorithm iteratively adjusts radiometric values by updating normalization parameters (slope α and intercept β).
  • The algorithm refines images until a satisfactory level of radiometric consistency is achieved.
  • The method was validated on multitemporal cross-sensor image datasets.

Main Results:

  • The proposed relaxation algorithm significantly improved radiometric consistency compared to original images and IR-MAD.
  • The method demonstrated superior performance in reducing radiometric inconsistencies.
  • Important image features were effectively preserved, and accuracy metrics showed improvement (MAE = 2.3, RMSE = 2.8).
  • Surface reflectance values exhibited enhanced consistency (R² = 87.56%, Euclidean distance = 2.11, spectral angle mapper = 12.60).

Conclusions:

  • The relaxation-based algorithm offers a robust solution for normalizing multitemporal cross-sensor satellite imagery.
  • This approach enhances the reliability of Earth surface monitoring by improving image comparability and analytical accuracy.
  • The algorithm provides a valuable tool for researchers and practitioners working with diverse satellite datasets.