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Local Spatial-Temporal Matching Method for Space-Based Infrared Aerial Target Detection.

Lue Chen1,2,3, Peng Rao1,2, Xin Chen1,2

  • 1Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai 200083, China.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary

A new local spatial-temporal matching (LSM) method effectively detects aerial targets in space-based infrared signals. LSM enhances weak targets against strong clutter, outperforming existing methods and reducing false alarms.

Keywords:
aerial target detectionlocal spatial–temporal matchingspace-based infrared detectorstaring imaging mode

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Area of Science:

  • Remote Sensing
  • Infrared Imaging
  • Signal Processing

Background:

  • Space-based infrared signals present a challenge for aerial target detection due to significantly stronger clutter intensity compared to targets.
  • Existing detection methods often amplify clutter, leading to missed targets and false alarms.

Purpose of the Study:

  • To propose a novel and concise method for aerial target detection in space-based infrared imagery.
  • To address the challenge of strong clutter overwhelming weak aerial targets in infrared signals.

Main Methods:

  • The proposed Local Spatial-Temporal Matching (LSM) method incorporates local normalization, direction matching, a spatial-temporal joint model, and inverse matching.
  • Local normalization equalizes target and clutter intensity, while direction matching estimates background movement.
  • The spatial-temporal joint model and inverse matching further enhance targets and suppress clutter.

Main Results:

  • Experiments on four space-based infrared datasets demonstrated LSM's effectiveness.
  • LSM successfully enhanced weak targets while suppressing strong clutter.
  • The method outperformed seven state-of-the-art techniques in aerial target detection.

Conclusions:

  • The Local Spatial-Temporal Matching (LSM) method provides a robust solution for aerial target detection in challenging space-based infrared environments.
  • LSM significantly improves detection accuracy by effectively handling strong clutter.
  • The proposed approach offers a promising advancement for space-based infrared surveillance and reconnaissance.