SARATR-X: A Foundation Model for Synthetic Aperture Radar Images Target Recognition

• URL: http://arxiv.org/abs/2405.09365v1
• Date: Wed, 15 May 2024 14:17:44 GMT
• Title: SARATR-X: A Foundation Model for Synthetic Aperture Radar Images Target Recognition
• Authors: Weijie L, Wei Yang, Yuenan Hou, Li Liu, Yongxiang Liu, Xiang Li,
• Abstract summary: This paper aims to achieve general SAR ATR based on a foundation model with Self-Supervised Learning (SSL) A foundation model named SARATR-X is proposed with the following four aspects. First, we integrated 14 datasets with various target categories and imaging conditions as a pre-training dataset. Second, different model backbones were discussed to find the most suitable approaches for remote-sensing images. Third, we applied two-stage training and SAR gradient features to ensure the diversity and scalability of SARATR-X.
• Score: 19.776275680586977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic aperture radar (SAR) is essential in actively acquiring information for Earth observation. SAR Automatic Target Recognition (ATR) focuses on detecting and classifying various target categories under different image conditions. The current deep learning-based SAR ATR methods are typically designed for specific datasets and applications. Various target characteristics, scene background information, and sensor parameters across ATR datasets challenge the generalization of those methods. This paper aims to achieve general SAR ATR based on a foundation model with Self-Supervised Learning (SSL). Our motivation is to break through the specific dataset and condition limitations and obtain universal perceptual capabilities across the target, scene, and sensor. A foundation model named SARATR-X is proposed with the following four aspects: pre-training dataset, model backbone, SSL, and evaluation task. First, we integrated 14 datasets with various target categories and imaging conditions as a pre-training dataset. Second, different model backbones were discussed to find the most suitable approaches for remote-sensing images. Third, we applied two-stage training and SAR gradient features to ensure the diversity and scalability of SARATR-X. Finally, SARATR-X has achieved competitive and superior performance on 5 datasets with 8 task settings, which shows that the foundation model can achieve universal SAR ATR. We believe it is time to embrace fundamental models for SAR image interpretation in the era of increasing big data.

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