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2026 Vol.3, Issue 1
A comprehensive survey on AI-based infrared satellite image analysis: From radiative principles to multi-task detection
Dongik Lee1
,
Bomin Kim1
,
Hyeonji Choi1
,
Sungho Kim1*
1Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
*Corresponding Author
30 June 2026. pp. 1-12
PDF XML Citation
Abstract

As national security threats diversify and the demand for persistent surveillance grows, infrared (IR) satellite imagery has become increasingly important because it enables day-and-night observation and provides thermal information complementary to visible imagery. The rapid advancement of deep learning has further shifted IR image analysis from conventional methods to data-driven intelligent detection. However, existing surveys are mostly limited to specific tasks, lacking a comprehensive review of the full AI-based IR image analysis pipeline. This paper presents an integrated survey spanning from the physical principles of IR radiation and preprocessing to AI-based detection and applications. Radiative transfer fundamentals and band-dependent sensing characteristics are first reviewed, followed by an examination of how physics-based atmospheric correction and deep learning-based restoration methods jointly enhance image reliability. For detection, we classify technologies into four tasks: object detection, infrared small target detection (IRSTD), change detection, and anomaly detection, and systematically compare key algorithms, benchmark datasets, and performance for each. Through this comprehensive analysis, we identify key open challenges and future directions for advancing satellite-based IR detection toward operational deployment.

Keywords
Infrared
Infrared search and track
Radiation
Target detection
Deep learning
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Information
  • Publisher :Korean Academy of Space Security
  • Publisher(Ko) :한국우주안보학회
  • Journal Title :JOURNAL OF SPACE SECURITY
  • Journal Title(Ko) :한국우주안보학회지
  • Volume : 3
  • No :1
  • Pages :1-12
  • Received Date : 2026-02-14
  • Revised Date : 2026-04-23
  • Accepted Date : 2026-05-16
  • DOI :https://doi.org/10.23386/joss.2026.3.1.001
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