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Cloud Detection: An Assessment Study from the ESA Round Robin Exercise for PROBA-V.

Umberto Amato1, Anestis Antoniadis2, Maria Francesca Carfora3

  • 1Istituto di Scienze Applicate e Sistemi Intelligenti 'E. Caianiello' CNR, 80131 Napoli, Italy.

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|April 12, 2020
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Summary

This study assesses cloud detection methods for PROBA-V satellite images, finding semi-automatic training data effective and comparing classification techniques for improved remote sensing accuracy.

Keywords:
K-Nearest NeighborPROBA-Vcloud detectioncumulative discriminant analysismachine learningneural networksstatistical learning

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Area of Science:

  • Earth Observation
  • Remote Sensing
  • Image Classification

Background:

  • ESA's Round Robin exercise compared cloud detection methods for PROBA-V sensor data.
  • A reliable dataset of 1350 labeled reflectances was created for this exercise.

Purpose of the Study:

  • To fully assess a proposed cloud detection methodology.
  • To investigate cloud detection challenges with limited spectral bands (visible and near-infrared).
  • To compare various classification methods for satellite image analysis.

Main Methods:

  • Utilized a high-quality, annotated dataset from ESA's PROBA-V sensor.
  • Demonstrated feasibility of semi-automatically generated training datasets.
  • Investigated the impact of ancillary data (surface type, climate) on cloud detection accuracy.
  • Compared multiple classification algorithms using consistent training data and configurations.
  • Performed consensus analysis to evaluate agreement among methods.

Main Results:

  • Semi-automatic training data generation is feasible for cloud detection.
  • Ancillary information can influence the accuracy of cloud detection algorithms.
  • Different classification methods exhibit varying performance in clear/cloudy sky detection.
  • Consensus analysis revealed degrees of agreement among the evaluated methods.

Conclusions:

  • The study provides a comprehensive assessment of cloud detection methodologies for PROBA-V data.
  • Findings highlight the utility of semi-automatic training and the impact of ancillary data.
  • Recommendations for selecting and configuring classification methods for satellite-based cloud detection are implied.