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A Long Time-Series Radiometric Normalization Method for Landsat Images.

Wei Wu1, Xia Sun2, Xianwei Wang3

  • 1College of Computer Science and Technology, Zhejiang University of Technology; Hangzhou 310023, China. wuwei@zjut.edu.cn.

Sensors (Basel, Switzerland)
|December 22, 2018
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Summary

This study introduces a new optimization strategy for radiometric normalization of long time-series remote sensing images, improving consistency and preserving vegetation data. The method effectively eliminates radiometric distortion, offering smoother time-series profiles and better overall performance than traditional approaches.

Keywords:
cloud and cloud shadowinflexion-based cloud detectionlong time-seriespseudo-invariant featuresradiometric normalization

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Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Image Processing

Background:

  • Radiometric normalization corrects distortions from atmospheric and sensor factors in remote sensing images.
  • Traditional pair-wise normalization methods struggle with long time-series data, leading to inconsistent feature extraction and obscured temporal trends.
  • Existing methods can introduce abrupt changes between temporally adjacent images, impacting the analysis of indicators like vegetation indices.

Purpose of the Study:

  • To develop an advanced optimization strategy for radiometric normalization of long time-series remote sensing imagery.
  • To overcome limitations of traditional methods in handling pseudo-invariant features (PIFs) and temporal residuals.
  • To enhance radiometric consistency and preserve crucial information, such as vegetation dynamics, within time-series datasets.

Main Methods:

  • Pixel time-series gray-scale values in the near-infrared band are sorted and segmented.
  • A modified Inflexion Based Cloud Detection (IBCD) method identifies outliers and inliers.
  • Pseudo-invariant features (PIFs) are identified based on their variation amplitudes, and a novel optimization strategy minimizes residuals for sequential normalization.

Main Results:

  • The proposed method effectively eliminates radiometric distortion in Landsat 5 Thematic Mapper time-series images.
  • It successfully preserves vegetation variation within the time-series data.
  • Smoother gray-scale value profiles and uniform root mean square error distributions were achieved compared to traditional methods.

Conclusions:

  • The novel optimization strategy significantly improves radiometric consistency and normalization performance for long time-series remote sensing data.
  • The method provides a more reliable basis for analyzing temporal changes and phenomena, such as vegetation health.
  • This approach offers a superior alternative to traditional radiometric normalization techniques for extensive image archives.