arXiv Computer Vision
ReFlowSET: Representation-Aligned Latent Flow Matching for SAR-to-EO Image Translation
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arXiv Computer Vision
Sep 2
C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation
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.
arXiv Computer Vision
Sep 30
GeoSET: Generalist Foundation Model for SAR-to-EO Image Translation
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...
arXiv Machine Learning
Sep 10
Cross-modal learning for SAR target recognition using optical vision foundation models
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.
arXiv Computer Vision
Oct 8
SAREO-FM: Decoupled Semantic Supervision for SAR-EO Foundation Models
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.
arXiv Computer Vision
Sep 17
SARATR-X-v2: Scale-Aware Structural Pre-Training for SAR Foundation Models
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...
arXiv Computer Vision
Sep 28
ContraFM-S2O: Flow Matching-Based One-step SAR-to-Optical Image Translation Model with Contrastive Learning
arXiv:2609.31378v1 Announce Type: new Abstract: In recent years, diffusion models and GAN-based models have become the mainstream approaches for SAR-to-optical image translation, owing to their advan...