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SPIN: cleaning, monitoring, and querying image streams generated by ground-based telescopes for space situational awareness

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Abstract

With the increasing number of objects in earth orbits, space situational awareness (SSA) becomes critical to space safety. As an economical option, ground-based telescopes can be deployed around the world and continuously provide imaginary information of space objects. However, they also raise unique challenges regarding big, noisy, and streaming data processing. In this paper, we present the SPIN system to address these challenges. The core algorithms process image sequences generated by ground-based telescopes and conduct: (1) image quality classification for data cleaning, (2) stream-based key-object identification and anomaly detection, and (3) efficient query processing on large image sequence repositories. Our goal is to design or adopt algorithms that handle the domain-specific image streams most efficiently and effectively. We use a 17-inch telescope to collect a large real dataset for evaluating the core algorithms, which covers more than ten satellites in one month and contains about 16,400 images. The experimental results show that the developed algorithms are fast enough for stream-based real-time processing and also yield high-quality results for all the primary tasks.

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Notes

  1. Satellites crash in space, debris scatters, http://www.tomsguide.com/us/Space-Satellite-Collision-Sibera,news-3477.html.

  2. A high-end GBT costs only about $50 K (check planewave.com).

  3. http://planewave.com/products-page/telescopes/.

  4. http://goo.gl/oNlRjS.

  5. The shape information may be available for low-orbit objects captured by radars [4], which, however, are not the objects targeted by ground-based telescopes, due to the objects’ high orbital velocity.

  6. http://solarsystem.nasa.gov/basics/bsf5-1.php.

  7. For an image of size \(x\times y\), the maximum distance is \(\sqrt{x^2+y^2}\). Each computed distance is divided by the maximum distance to get the normalized value.

  8. fits.gsfc.nasa.gov/.

  9. The dataset is first labeled by one editor. Then, the other editor verifies the result and identifies the inconsistent ones. Finally, the inconsistent ones are reviewed by both editors and assigned to the agreed category.

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Acknowledgements

This project was supported by a DAGSI/AFRL award. Many thanks to the AFRL colleagues who helped set up the instruments and collect the images.

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Authors and Affiliations

  1. Data Intensive Analysis and Computing Lab, Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA

    Keke Chen, Bharath Avusherla & Sarah Allison

  2. Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH, USA

    Vincent Schmidt

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  1. Keke Chen
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  2. Bharath Avusherla
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  3. Sarah Allison
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  4. Vincent Schmidt
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Correspondence to Keke Chen.

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Chen, K., Avusherla, B., Allison, S. et al. SPIN: cleaning, monitoring, and querying image streams generated by ground-based telescopes for space situational awareness. Int J Data Sci Anal 5, 155–167 (2018). https://doi.org/10.1007/s41060-017-0071-0

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