SARATR-X: Toward Building a Foundation Model for SAR Target Recognition

Despite the remarkable progress in synthetic aperture radar automatic target recognition (SAR ATR), recent efforts have concentrated on detecting and classifying a specific category, e.g., vehicles, ships, airplanes, or buildings. One of the fundamental limitations of the top-performing SAR ATR methods is that the learning paradigm is supervised, task-specific, limited-category, closed-world learning, which depends on massive amounts of accurately annotated samples that are expensively labeled by expert SAR analysts and have limited generalization capability and scalability. In this work, we make the first attempt towards building a foundation model for SAR ATR, termed SARATR-X. SARATR-X learns generalizable…

• NN
  National Natural Science Foundation of China

  Award: 62376283
• NU
  National University of Defense Technology

  Award: JS2023-03
• NS
  Natural Science Foundation for Distinguished Young Scholars of Hunan Province

  Award: 2024JJ2066