Abstract In recent years, the heterogeneous SAR image classification task of "training on simulated data and testing on measured data" has garnered increasing attention in the field of Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR). Although current mainstream domain adaptation methods have made significant breakthroughs in addressing domain shift problems, the escalating model complexity and task complexity have constrained their deployment in real-world applications. To tackle this challenge, this paper proposes a domain adaptation framework based on linear-kernel Maximum Mean Discrepancy (MMD),integrated with a near-zero-cost pseudo-label denoising technique leveraging deep feature clustering. Our method completely eliminates the need for data augmentation and handcrafted feature design, achieving endto-end pseudo-label self-training. Competitive performance is demonstrated across three typical scenarios in the SAMPLE dataset, with the highest accuracy of 98.65% achieved in Scenario III. The relevant code is available at: https://github.com/TheGreatTreatsby/SAMPLE_MMD.
Corresponding Authors: Liu Zhi-Gang (Email: dennylzg@163.com).
E-mail: dennylzg@163.com
About author: Luo Sheng-Jie, Ph.D. candidate, obtained a master's degree in nuclear science and technology from Rocket Force University of Engineering in 2023. At present, he is studying for a doctorate in nuclear science and technology at Rocket Force University of Engineering. Committed to the research of SAR target automatic recognition and SAR image simulation
Cite this article:
. Domain Adaptation with Deep Feature Clustering for Pseudo-Label Denoising in Heterogeneous SAR Image Classification[J]. APPLIED GEOPHYSICS, 2025, 22(4): 944-956.
2025, Vol. 22 
