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Lidar detection algorithm for time and range anomalies.

Avishai Ben-David1, Charles E Davidson, Richard G Vanderbeek

  • 1RDECOM, Edgewood Chemical Biological Center, Aberdeen Proving Ground, Maryland 21010, USA. avishai.bendavid@us.army.mil

Applied Optics
|October 13, 2007
PubMed
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A novel hyperspectral anomaly detection algorithm identifies aerosol clouds using lidar. This method enhances target detection in lidar data by analyzing spatial and temporal anomalies, improving signal visualization.

Area of Science:

  • Atmospheric Science
  • Optical Remote Sensing
  • Signal Processing

Background:

  • Lidar (light detection and ranging) systems are crucial for atmospheric monitoring.
  • Detecting faint aerosol clouds in lidar data presents challenges due to noise and background interference.
  • Existing methods may struggle with accurate spatiotemporal anomaly detection.

Purpose of the Study:

  • To develop and validate a new hyperspectral anomaly detection algorithm for lidar applications.
  • To address both time and range anomalies for robust target identification.
  • To improve the detection of aerosol clouds in complex lidar measurements.

Main Methods:

  • Hyperspectral anomaly detection applied to time and range dimensions.
  • Low-rank orthogonal projection for preprocessing, filtering artifacts, and noise reduction.

Related Experiment Videos

  • Gaussian-mixture probability model with expectation-maximization for threshold determination.
  • Analysis of CO2 lidar measurements for bioaerosol clouds (Bacillus atrophaeus, Pantoea agglomerans).

    • Main Results:

    • The algorithm successfully identifies anomalies indicative of aerosol cloud presence.
    • Preprocessing effectively removes background noise and enhances low signal-to-noise ratio data.
    • The detection score reliably distinguishes target signals from background measurements.
    • Probabilities of detection and false alarm were computed using a probabilistic model.

    Conclusions:

    • The developed hyperspectral anomaly detection algorithm offers a robust method for lidar-based aerosol cloud detection.
    • The preprocessing stage significantly improves data quality and feature visualization.
    • The algorithm demonstrates effectiveness in analyzing CO2 lidar measurements for bioaerosol identification.