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: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
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
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:2607. 16819v1 Announce Type: new Abstract: In recent years, large-scale vision-language models have been driving a paradigm shift in intelligent remote sensing image interpretation.
By Yi Yang, Xiaokun Zhang, Yuxuan Li, Ruyi Zhang, Xinpeng Zhou, Haipeng Wang
The paper introduces BMT, a unified hierarchical Vision Transformer that jointly performs SAR-to-optical image translation and semantic segmentation. It incorporates a LocalViTBlock, an enhanced output module, a ControlNet-style conditional injection, and a bounded Kendall uncertainty weighting scheme to balance the two tasks. Experiments on paired and unpaired datasets demonstrate competitive performance in both translation quality and segmentation accuracy.
By Siyuan Liu, Xuze Zhang, Yongshun Wang, Licong Pan, Hang Liu, Huihui Li