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A SLIC-DBSCAN Based Algorithm for Extracting Effective Sky Region from a Single Star Image.

Chenguang Shi1,2, Rui Zhang1,2, Yong Yu1,2

  • 1Innovation Academy for Microsatellites of Chinese Academy of Sciences, Room 426, Building 4, 99 Haike Road, Shanghai 201203, China.

Sensors (Basel, Switzerland)
|September 10, 2021
PubMed
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This study introduces a novel algorithm using Simple Linear Iterative Clustering (SLIC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to remove interference in star tracker images. The method effectively extracts star data even with significant background noise and large-area interference.

Area of Science:

  • Aerospace Engineering
  • Computer Vision
  • Signal Processing

Background:

  • Star trackers are crucial for high-accuracy space missions, offering precise positioning and autonomy.
  • Interference suppression in star tracker imagery remains a significant technical challenge.
  • Existing methods struggle with effectively removing large-area interference while preserving star data.

Purpose of the Study:

  • To develop and validate a robust algorithm for extracting star information from images corrupted by strong interference.
  • To enhance the reliability and accuracy of star trackers in challenging operational environments.
  • To address the limitations of current interference suppression techniques in star tracking.

Main Methods:

  • A novel algorithm combining Simple Linear Iterative Clustering (SLIC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was developed.
Keywords:
interference suppressionstar image processingstar trackervision saliency

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  • Restricted luminance-based contrast (LC) transformation was used to enhance image contrast and differentiate interference.
  • SLIC algorithm with optimized parameters segmented saliency maps, followed by DBSCAN for feature extraction and superpixel clustering.

    • Main Results:

    • The proposed SLIC-DBSCAN algorithm successfully removed large-area interference from star tracker images.
    • Effective extraction of star spots was achieved even in the presence of significant background noise.
    • Validation on real star images demonstrated the algorithm's practical efficacy.

    Conclusions:

    • The developed SLIC-DBSCAN algorithm provides an effective solution for interference suppression in star tracker imagery.
    • This method significantly improves the quality of star data extraction under adverse conditions.
    • The findings contribute to enhancing the performance and reliability of autonomous space navigation systems.