mlsanityData to 7 Oct 2026

Diffusion Models for Interferometric Satellite Aperture Radar

arXiv 2308.16847 ↗First posted 31 Aug 2023cs.CV cs.LG eess.IVcited by 2

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

Probabilistic Diffusion Models (PDMs) have recently emerged as a very promising class of generative models, achieving high performance in natural image generation. However, their performance relative to non-natural images, like radar-based satellite data, remains largely unknown. Generating large amounts of synthetic (and especially labelled) satellite data is crucial to implement deep-learning approaches for the processing and analysis of (interferometric) satellite aperture radar data. Here, we leverage PDMs to generate several radar-based satellite image datasets. We show that PDMs succeed in generating images with complex and realistic structures, but that sampling time remains an issue. Indeed, accelerated sampling strategies, which work well on simple image datasets like MNIST, fail on our radar datasets. We provide a simple and versatile open-source https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation to train, sample and evaluate PDMs using any dataset on a single GPU.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 35 of its 77 references that are in the map. First posted on arXiv 31 Aug 2023.

Builds on

35 of 77 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion10 Apr

Other references · 34

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2022Generative Adversarial Networkscited by 5,603
  8. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  9. 2013Auto-Encoding Variational Bayescited by 3,980
  10. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  11. 2016Improved Techniques for Training GANscited by 2,938
  12. 2016Density estimation using Real NVPcited by 2,029
22 more
  1. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  2. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  3. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  4. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 925
  5. 2016Pixel Recurrent Neural Networkscited by 814
  6. 2022Pseudo Numerical Methods for Diffusion Models on Manifoldscited by 695
  7. 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributionscited by 566
  8. 2022Fast Sampling of Diffusion Models with Exponential Integratorcited by 526
  9. 2018Assessing Generative Models via Precision and Recallcited by 401
  10. 2022Understanding Diffusion Models: A Unified Perspectivecited by 337
  11. 2022Diffusion Probabilistic Modeling for Video Generationcited by 237
  12. 2021Score-Based Generative Modeling with Critically-Damped Langevin Diffusioncited by 230
  13. 2018A Note on the Inception Scorecited by 225
  14. 2021UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Modelscited by 133
  15. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 121
  16. 2022Accelerating Diffusion Models via Early Stop of the Diffusion Processcited by 100
  17. 2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
  18. 2018An empirical study on evaluation metrics of generative adversarial networkscited by 81
  19. 2023Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEscited by 54
  20. 2022Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoderscited by 42
  21. 2019Temporal phase unwrapping using deep learningcited by 7
  22. 2020A Joint Convolutional and Spatial Quad-Directional LSTM Network for Phase Unwrappingcited by 6

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Cited by year

Citing papers per year: 2024: 2.

2 papers.

  1. 2024Diffusion Models in Low-Level Vision: A Surveycited by 73
  2. 2024Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectivescited by 27

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  1. 2024SAR Image Synthesis with Diffusion Models
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  3. 2024SAR to Optical Image Translation with Color Supervised Diffusion Model
  4. 2025SAR-to-RGB Translation with Latent Diffusion for Earth Observation
  5. 2023DiffusionSat: A Generative Foundation Model for Satellite Imagery
  6. 2025Diffusion Probabilistic Models for Compressive SAR Imaging
  7. 2025Diffusion-Prior Split Gibbs Sampling for Synthetic Aperture Radar Imaging under Incomplete Measurements
  8. 2025COP-GEN-Beta: Unified Generative Modelling of COPernicus Imagery Thumbnails