GeoDiff-SAR: A Geometric Prior Guided Diffusion Model for SAR Image Generation
College of Information Science and Technology, Beijing University of Chemical Technology（信息科学与技术学院，北京化工大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（航天信息研究所，中国科学院）
Fan Zhang, Xuanting Wu, Fei Ma, Qiang Yin, Yuxin Hu
Synthetic aperture radar (SAR) image generation can mitigate data scarcity, but controllablegeneration under sparse observation angles remains difficult. Recent SAR generative studies im-prove texture realism, yet explicit geometry-aware control is still limited. This paper studiesthe focused and verifiable setting of intermediate-azimuth completion: 3D-model-derived geo-metric priors guide a diffusion model to synthesize the views missing from sparse-angle trainingdata. GeoDiff-SAR constructs a lightweight multi-bounce ray-tracing prior, encodes the result-ing point cloud, and fuses it with text conditioning while adapting Stable Diffusion 3.5 Mediumthrough low-rank adaptation. On a real four-category aircraft dataset, GeoDiff-SAR reaches anSSIM of 0.812 and azimuth consistency of 0.940, compared with 0.738 and 0.782 for the text-conditioned SD3.5 Medium baseline. The same sparse-angle protocol on five MSTAR vehicleclasses yields an SSIM of 0.878 and azimuth consistency of 0.917. These results support theconclusion that a lightweight 3D geometric prior improves viewpoint adherence for controllableSAR generation; it is intended as generation guidance rather than high-fidelity electromagneticreconstruction.
PriSAR: 3D Geometric-Prior-Guided Diffusion for Parameter-Controlled SAR Image Generation
College of Information Science and Technology, Beijing University of Chemical Technology（北京化工大学信息科学与技术学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息研究院）
Unpaired Object-Level SAR-to-Optical Image Translation for Aircraft with Keypoints-Guided Diffusion Models
School of Information Science and Technology, Fudan University（复旦大学信息科学与技术学院）；Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education)（电磁波信息科学教育部重点实验室）
Domain Adaptive SAR Wake Detection: Leveraging Similarity Filtering and Memory Guidance
College of Computer Science and Technology, Qingdao University（青岛大学计算机科学与技术学院）；Laboratory for Regional Oceanography and Numerical Modeling, Qingdao Marine Science and Technology Center（青岛海洋科学与技术中心区域海洋学与数值模拟实验室）；School of Computer Science and Information Technology, The University of Nottingham（诺丁汉大学计算机科学与信息学院）；State Key Laboratory of Physical Oceanography, Department of Marine Technology, Ocean University of China（中国海洋大学物理海洋学国家重点实验室，海洋技术系）
OSDM-MReg: Multimodal Image Registration based One Step Diffusion Model
Shanghai Key Laboratory of Intelligent Sensing and Recognition（上海智能感知与识别重点实验室）；Shanghai Jiao Tong University（上海交通大学）；Center of Digital Innovation（数字创新中心）
Seg-CycleGAN : SAR-to-optical image translation guided by a downstream task
Gina Cody School of Engineering and Computer Science, Concordia University（康考迪亚大学吉娜·科迪工程与计算机科学学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络空间安全学院）
Northwestern Polytechnical University（西北工业大学）；Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR)（德国航空航天中心（DLR）遥感技术研究所（IMF））；University POLITEHNICA of Bucharest (UPB)（布加勒斯特理工大学（UPB））
Integrating the special electromagnetic characteristics of Synthetic Aperture Radar (SAR) in deep neural networks is essential in order to enhance the explainability and physics awareness of deep learning. In this paper, we first propose a novel physically explainable convolutional neural network for SAR image classification, namely physics guided and injected learning (PGIL). It comprises three parts: (1) explainable models (XM) to provide prior physics knowledge, (2) physics guided network (PGN) to encode the knowledge into physics-aware features, and (3) physics injected network (PIN) to ad…
New Fourth-Order Grayscale Indicator-Based Telegraph Diffusion Model for Image Despeckling
School of Mathematical and Statistical Sciences, Indian Institute of Technology Mandi（印度理工学院曼迪数学与统计科学学院）；Department of Mathematics, Indian Institute of Technology Delhi（印度理工学院德里数学系）
MoSa: Motion Generation with Scalable Autoregressive Modeling
State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School（人工智能通用基础理论国家重点实验室，北京大学深圳研究生院）；Chongqing University of Technology（重庆理工大学）；Tencent Technology Co., Ltd.（腾讯科技有限公司）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，中国科学院自动化研究所）
Task-Guided Prompting for Unified Remote Sensing Image Restoration
School of Electronic and Information Engineering, Ningbo University of Technology（宁波工程学院电子与信息工程学院）；University of Exeter（埃克塞特大学）；Engineering Research Center of Intelligent Finance, Ministry of Education（教育部智能金融工程研究中心）；School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机与人工智能学院）
In this study we investigate the potential for using synthetic aperture radar (SAR) data to provide high resolution defoliation and regrowth mapping of trees in the tundra-forest ecotone. Using aerial photographs, four areas with live forest and four areas with dead trees were identified. Quad-polarimetric SAR data from RADARSAT-2 was collected from the same area, and the complex multilook polarimetric covariance matrix was calculated using a novel extension of guided nonlocal means speckle filtering. The nonlocal approach allows us to preserve the high spatial resolution of single-look comple…
