SARATR-X-v2: Scale-Aware Structural Pre-Training for SAR Foundation Models

arXiv:2607.23238 · cs.CV · Submitted 2026-07-25 · Read on arXiv

cs.CV

Submitted: 2026-07-25

Updated: 2026-09-26

Code: https://github.com/waterdisappear/SARATR-X-v2

Terminology

• Benchmark — 49× in this paper · explained in 2 episodes
• Foundation Model — 34× in this paper · explained in 3 episodes
• Accuracy — 21× in this paper · explained in 1 episode
• Foundation Models — 19× in this paper · explained in 7 episodes
• Transformer — 18× in this paper · explained in 2 episodes
• Aperture — 10× in this paper · explained in 1 episode
• Downstream Tasks — 10× in this paper · explained in 2 episodes
• Representation Learning — 10× in this paper · explained in 2 episodes
• Robustness — 10× in this paper · explained in 5 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 6× in this paper
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Linear Probing — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Transferability — 5× in this paper · explained in 4 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Self-Supervised Learning — 3× in this paper · explained in 8 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Linear probe — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptive Fusion Mechanism — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• PID Controller — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes

Sources

• HuiYanEarth-SAR: A Foundation Model for High-Fidelity and Low-Cost Global Remote Sensing Imagery Generation
• FUSAR-KLIP: Towards Multimodal Foundation Models for Remote Sensing
• SARMAE: Masked Autoencoder for SAR Representation Learning
• CrossEarth-SAR: A SAR-Centric and Billion-Scale Geospatial Foundation Model for Domain Generalizable Semantic Segmentation
• SAMBA: A Scatter-Guided Masked Bidirectional Mamba Foundation Model for SAR Target Recognition
• A Complex-valued SAR Foundation Model Based on Physically Inspired Representation Learning
• M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detection
• SARVLM: A Vision Language Foundation Model for Semantic Understanding in SAR Imagery
• Visual Instruction Pretraining for Domain-Specific Foundation Models
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
• VMamba: Visual State Space Model

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