Quantitative Comparison of Fine-Tuning Techniques for Pretrained Latent Diffusion Models in the Generation of Unseen SAR Image Concepts

This work investigates the adaptation of large pre-trained latent diffusion models to a radically new imaging domain: Synthetic Aperture Radar (SAR). While these generative models, originally trained on natural images, demonstrate impressive capabilities in text-to-image synthesis, they are not natively adapted to represent SAR data, which involves different physics, statistical distributions, and visual characteristics. Using a sizeable SAR dataset (on the order of 100,000 to 1 million images), we address the fundamental question of fine-tuning such models for this unseen modality. We explore and compare multiple fine-tuning strategies, including full model fine-tuning and parameter-efficient approaches like Low-Rank Adaptation (LoRA), focusing separately on the UNet diffusion backbone and the text encoder components. To evaluate generative quality, we combine several metrics: statistical distance from real SAR distributions, textural similarity via GLCM descriptors, and semantic alignment assessed with a CLIP model fine-tuned on SAR data. Our results show that a hybrid tuning strategy yields the best performance: full fine-tuning of the UNet is better at capturing low-level SAR-specific patterns, while LoRA-based partial tuning of the text encoder, combined with embedding learning of the <SAR> token, suffices to preserve prompt alignment. This work provides a methodical strategy for adapting foundation models to unconventional imaging modalities beyond natural image domains.

From Spaceborne to Airborne: SAR Image Synthesis Using Foundation Models for Multi-Scale Adaptation

The availability of Synthetic Aperture Radar (SAR) satellite imagery has increased considerably in recent years, with datasets commercially available. However, the acquisition of high-resolution SAR images in airborne configurations, remains costly and limited. Thus, the lack of open source, well-labeled, or easily exploitable SAR text-image datasets is a barrier to the use of existing foundation models in remote sensing applications. In this context, synthetic image generation is a promising solution to augment this scarce data, enabling a broader range of applications. Leveraging over 15 years of ONERA's extensive archival airborn data from acquisition campaigns, we created a comprehensive training dataset of 110 thousands SAR images to exploit a 3.5 billion parameters pre-trained latent diffusion model \cite{Baqu2019SethiR}. In this work, we present a novel approach utilizing spatial conditioning techniques within a foundation model to transform satellite SAR imagery into airborne SAR representations. Additionally, we demonstrate that our pipeline is effective for bridging the realism of simulated images generated by ONERA's physics-based simulator EMPRISE \cite{empriseem_ai_images}. Our method explores a key application of AI in advancing SAR imaging technology. To the best of our knowledge, we are the first to introduce this approach in the literature.

From Spaceborn to Airborn: SAR Image Synthesis Using Foundation Models for Multi-Scale Adaptation

The availability of Synthetic Aperture Radar (SAR) satellite imagery has increased considerably in recent years, with datasets commercially available. However, the acquisition of high-resolution SAR images in airborne configurations, remains costly and limited. Thus, the lack of open source, well-labeled, or easily exploitable SAR text-image datasets is a barrier to the use of existing foundation models in remote sensing applications. In this context, synthetic image generation is a promising solution to augment this scarce data, enabling a broader range of applications. Leveraging over 15 years of ONERA's extensive archival airborn data from acquisition campaigns, we created a comprehensive training dataset of 110 thousands SAR images to exploit a 3.5 billion parameters pre-trained latent diffusion model. In this work, we present a novel approach utilizing spatial conditioning techniques within a foundation model to transform satellite SAR imagery into airborne SAR representations. Additionally, we demonstrate that our pipeline is effective for bridging the realism of simulated images generated by ONERA's physics-based simulator EMPRISE. Our method explores a key application of AI in advancing SAR imaging technology. To the best of our knowledge, we are the first to introduce this approach in the literature.

The speckle contrast extended to the polarimetric case: applications to radar and Laser images

This article proposes the application of various alternative definitions of the multivariate coefficient of variation parameter in two domains: radar polarimetric time series and dynamic polarimetric speckle. In the first case, the focus is on detecting permanent scatterers or changes, while in the second case, it involves calculating activity images. Our study demonstrates that most of these parameters offer added value in terms of signal-to-noise ratio improvement and enhancing contrast in specific regions. Furthermore, the concept of polarimetric multivariate coefficient of variation proves to be closely related to the degree of polarization.