Frontiers of Data and Computing ›› 2025, Vol. 7 ›› Issue (4): 143-154.

CSTR: 32002.14.jfdc.CN10-1649/TP.2025.04.012

doi: 10.11871/jfdc.issn.2096-742X.2025.04.012

• Special Issue: Artificially Intelligent Models and Tools for Space Science Big Data • Previous Articles     Next Articles

Research on Anomaly Detection Algorithms for Scientific Satellite Images Based on PatchCore

WANG Lei1,2(),MA Fuli1,2,*(),YU Qinsi1,2,WEI Mingyue1,2   

  1. 1. National Space Science Center, CAS, Research Laboratory for Spatial Big Data Technology, Beijing 100190, China
    2. National Space Science Data Center, Beijing 100190, China
  • Received:2025-04-29 Online:2025-08-20 Published:2025-08-21
  • Contact: MA Fuli E-mail:wanglei01@nssc.ac.cn;mafuli007@nssc.ac.cn

Abstract:

[Objective] With the rapid growth of observational data from space science satellites, image anomaly detection has become a critical component for ensuring data quality and supporting scientific research, which requires urgently the development of efficient and automated methods. [Context] Due to the scarcity or even absence of anomaly samples during the early stages of satellite operation, traditional supervised learning approaches are difficult to apply directly. Therefore, this study proposes an anomaly detection framework tailored for space science satellite images based on unsupervised learning. [Methods] Based on the PatchCore algorithm, the proposed method designs modules for feature extraction, core-set construction, anomaly scoring, and image classification. In addition, multiple anomaly scoring and threshold setting strategies based on statistical analysis and clustering methods are explored to enhance detection sensitivity and robustness. [Results] Extensive experiments were conducted on actual solar observation datasets, with comparative analysis against mainstream unsupervised detection methods such as PaDiM and CS-Flow. The results demonstrate that the proposed method achieves outstanding performance, with AUROC and AUPR values reaching to 0.9996 and 0.9999, respectively. In terms of system implementation, ONNX Runtime is adopted for lightweight model deployment, which significantly improves inference speed and deployment flexibility, and establishes a complete closed-loop process covering data acquisition, anomaly detection, and alert feedback. [Conclusions] The study shows that the developed system can effectively enhance the efficiency of image quality monitoring in space science missions, providing valuable references for the construction of intelligent anomaly detection systems in future space observation tasks.

Key words: unsupervised learning, PatchCore, space science satellites, image anomaly detection