The paper proposes a cross‑modal framework that uses a frozen DINOv3 optical vision foundation model to create class‑level prototypes for Synthetic Aperture Radar (SAR) target recognition. By aligning SAR embeddings to these optical prototypes, the SAR model learns to classify SAR images without needing paired optical data. Experiments on the UNICORNv2 dataset show that this prototype alignment improves SAR classification accuracy compared to baseline methods and yields clearer class separation in the embedding space.
By Lucas Hirsch, James R. Hopgood, Javid Khan, Yoann Altmann, Mike E. Davies
arXiv:2510. 22665v4 Announce Type: replace-cross Abstract: Synthetic Aperture Radar (SAR) is a critical imaging modality due to its all-weather operational capability.
By Qiwei Ma, Xukun Lu, Wang Liu, Puhong Duan, Xudong Kang, Shutao Li
arXiv:2607.23238v3 Announce Type: replace
Abstract: Masked image modeling has become a dominant paradigm for SAR pre-training, yet the design of the reconstruction target remains fundamentally unsett...
By Weijie Li, Yafei Song, Yongxiang Liu, Bowen Peng, Jie Zhou, Jingyuan Xia, Wei Yang, Tianpeng Liu, Zhen Liu, Li Liu
arXiv:2609.00968v1 Announce Type: new
Abstract: SAR-to-EO image translation aims to generate electro-optical (EO) imagery from synthetic aperture radar (SAR) observations. Existing latent diffusion a...
By Jeonghyeok Do, Seungchul Lee, Munchurl Kim
arXiv:2608.29384v1 Announce Type: cross
Abstract: The growing availability of dense commercial Synthetic Aperture Radar (SAR) time series enables temporal Interferometric SAR (InSAR) analysis, but fi...
By Getnet Demil, Muhammad Farhan Humayun, Tomi Westerlund, Jukka Heikkonen, Mourad Oussalah
arXiv:2609.37496v1 Announce Type: new
Abstract: Paired synthetic aperture radar (SAR) and electro-optical (EO) imagery is increasingly available across sensors, resolutions, and geographic regions. Y...
By Jeonghyeok Do, Munchurl Kim
SAREO‑FM is a foundation model that separates semantic supervision from reconstruction supervision in synthetic aperture radar (SAR) and electro‑optical (EO) imagery. It uses modality tokens for masked reconstruction and learnable semantic queries guided by a pretrained vision foundation model to capture scene content. Trained on the million‑scale SAR‑1M corpus, the model shows strong unimodal transfer for SAR and EO alone and significant improvements when both modalities are combined.
By Jeonghyeok Do, Munchurl Kim
arXiv:2606. 20523v1 Announce Type: cross Abstract: Multimodal foundation models have advanced rapidly thanks to large optical benchmarks, but comparable resources for synthetic aperture radar (SAR) remain limited.
By Sol\`ene Debuys\`ere, Nicolas Trouv\'e, Nathan Letheule, Elise Colin, Georgia Channing
ProSR is a new method for Synthetic Aperture Radar (SAR) super‑resolution that treats the task as a discrete token prediction problem in a quantized latent space. By mapping SAR signal features to discrete scattering primitives and using a self‑supervised backbone to extract label‑free semantic priors, ProSR preserves the impulsive, physically consistent scattering statistics of SAR images. The approach includes Semantic‑Aligned Detail Encoding and a Prototype‑Map‑Guided Attention mechanism, and it has been validated on a large‑scale 0.25 m resolution benchmark from the Umbra Open Dataset, achieving superior visual quality while maintaining essential scattering characteristics.
By Byoungwoo Kim, Munchurl Kim
Agricultural monitoring faces unique challenges, arising from the landscape's complex temporal, phenological, and climate dynamics, yet monitoring them is critical for ensuring food security. Synthetic Aperture Radar (SAR) satellites offer all-weather day-night imaging capability supporting key monitoring tasks including crop type mapping, yield prediction and phenological event detection.
arXiv:2511. 17126v4 Announce Type: replace-cross Abstract: Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations.
By Xiaolong Qian, Qi Jiang, Yao Gao, Lei Sun, Kailun Yang, Xian Wang, Zhonghua Yi, Wenyong Li, Ming-Hsuan Yang, Luc Van Gool, Kaiwei Wang
C‑DiffSET is a SAR‑to‑EO image translation framework that uses a pretrained Latent Diffusion Model to adapt SAR imagery to the EO domain. The method exploits the pretrained VAE encoder’s ability to map SAR and EO images into a shared latent space, even when SAR inputs contain varying noise levels. A confidence‑guided diffusion loss further improves pixel‑wise fidelity by reducing artifacts such as appearing or disappearing objects, leading to state‑of‑the‑art results across multiple datasets.
By Jeonghyeok Do, Jaehyup Lee, Munchurl Kim