mlsanityData to 7 Oct 2026

Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey

arXiv 2411.05027 ↗First posted 5 Nov 2024cs.CV cs.AI eess.IVcited by 9

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Abstract (arXiv)

SAR images possess unique attributes that present challenges for both human observers and vision AI models to interpret, owing to their electromagnetic characteristics. The interpretation of SAR images encounters various hurdles, with one of the primary obstacles being the data itself, which includes issues related to both the quantity and quality of the data. The challenges can be addressed using generative AI technologies. Generative AI, often known as GenAI, is a very advanced and powerful technology in the field of artificial intelligence that has gained significant attention. The advancement has created possibilities for the creation of texts, photorealistic pictures, videos, and material in various modalities. This paper aims to comprehensively investigate the intersection of GenAI and SAR. First, we illustrate the common data generation-based applications in SAR field and compare them with computer vision tasks, analyzing the similarity, difference, and general challenges of them. Then, an overview of the latest GenAI models is systematically reviewed, including various basic models and their variations targeting the general challenges. Additionally, the corresponding applications in SAR domain are also included. Specifically, we propose to summarize the physical model based simulation approaches for SAR, and analyze the hybrid modeling methods that combine the GenAI and interpretable models. The evaluation methods that have been or could be applied to SAR, are also explored. Finally, the potential challenges and future prospects are discussed. To our best knowledge, this survey is the first exhaustive examination of the interdiscipline of SAR and GenAI, encompassing a wide range of topics, including deep neural networks, physical models, computer vision, and SAR images. The resources of this survey are open-source at urlhttps://github.com/XAI4SAR/GenAIxSAR.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 114 of its 306 references that are in the map. First posted on arXiv 5 Nov 2024.

Builds on

114 of 306 references.

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50 most cited of 114 All 114 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 5.

9 papers.

  1. 2025ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wildcited by 12
  2. 2025Phi-GAN: Physics-Inspired GAN for Generating SAR Images Under Limited Datacited by 4
  3. 2025FUSAR-KLIP: Towards Multimodal Foundation Models for Remote Sensingcited by 2
  4. 2025Unpaired Object-Level SAR-to-Optical Image Translation for Aircraft with Keypoints-Guided Diffusion Modelscited by 0
  5. 2026GeoDiff-SAR: A Geometric Prior Guided Diffusion Model for SAR Image Generationcited by 0
  6. 2026MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modelingcited by 0
  7. 2026A Task-Driven and Quality-Assured Agent Framework for SAR Data Generationcited by 0
  8. 2026PriSAR: 3D Geometric-Prior-Guided Diffusion for Parameter-Controlled SAR Image Generationcited by 0
  9. 2026Learning the Target Priors Before Image Translation: A Decoupled Training Paradigm for Cross-Modal Image Translation in Remote Sensingcited by 0