卫星图像分析对土地利用、城市化和生态系统监测具有重要意义。深度学习方法可以通过支持不同卫星模式之间的知识转移来弥补个别缺点，从而促进对电光（EO）和合成孔径雷达（SAR）图像等不同卫星模式的分析。最近的进展表明，神经网络嵌入的分布对齐如何通过采用切片Wasserstein距离（SWD）损失来产生强大的迁移学习模型。我们分析了如何将这种方法应用于哨兵-1和-2卫星图像，并开发了几个扩展，使其在实践中有效。在Few-Shot Local Climate Zone（LCZ）预测的应用中，我们证明了这些网络在具有大量类的数据集上优于多个常见基线。此外，我们提供的证据表明，实例规范化可以显着稳定的训练过程，并明确塑造嵌入空间使用监督对比学习可以提高性能。
英文摘要：
Satellite image analysis has important implications for land use, urbanization, and ecosystem monitoring. Deep learning methods can facilitate the analysis of different satellite modalities, such as electro-optical (EO) and synthetic aperture radar (SAR) imagery, by supporting knowledge transfer between the modalities to compensate for individual shortcomings. Recent progress has shown how distributional alignment of neural network embeddings can produce powerful transfer learning models by employing a sliced Wasserstein distance (SWD) loss. We analyze how this method can be applied to Sentinel-1 and -2 satellite imagery and develop several extensions toward making it effective in practice. In an application to few-shot Local Climate Zone (LCZ) prediction, we show that these networks outperform multiple common baselines on datasets with a large number of classes. Further, we provide evidence that instance normalization can significantly stabilize the training process and that explicitly shaping the embedding space using supervised contrastive learning can lead to improved performance.
Boosting ship detection in SAR images with complementary pretraining techniques
Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology（中国空间技术研究院钱学森空间技术实验室）；School of Information and Electronics Engineering, Beijing Institute of Technology（北京理工大学信息与电子工程学院）；School of Mathematics and Computational Science, Xiangtan University（湘潭大学数学与计算科学学院）
DI3CL: Contrastive Learning With Dynamic Instances and Contour Consistency for SAR Land-Cover Classification Foundation Model
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education（教育部智能感知与图像理解重点实验室）；International Research Center for Intelligent Perception and Computation（智能感知与计算国际研究中心）；Joint International Research Laboratory of Intelligent Perception and Computation（智能感知与计算联合国际实验室）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；Hangzhou Institute of Technology（杭州科技学院）
OpenEarthMap-SAR: A Benchmark Synthetic Aperture Radar Dataset for Global High-Resolution Land Cover Mapping
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN（理化学研究所先进智能中心（AIP），理化学研究所）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）
Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（信息工程测绘与遥感国家重点实验室）；School of Computer Science and Information Engineering（计算机科学与信息工程学院）；Hangzhou International Innovation Institute（杭州国际创新研究院）；Oriental Space Port Research Institute（东方航天港研究院）；Hubei Luojia Laboratory（湖北珞珈实验室）
Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data
RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Inria（法国国家信息与自动化研究所）；Munich University of Applied Sciences（慕尼黑应用科学大学）；the University of Tokyo（东京大学）
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；German Aerospace Center (DLR)（德国航空航天中心）
SAR-ShipNet: SAR-Ship Detection Neural Network via Bidirectional Coordinate Attention and Multi-resolution Feature Fusion
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术与产业化协同创新中心）
TAR: Text Semantic Assisted Cross-modal Image Registration Framework for Optical and SAR Images
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University（中国教育部智能感知与图像理解重点实验室，西安电子科技大学人工智能学院）；Department of Automation, Tsinghua University（清华大学自动化系）
Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product
University of California - Los Angeles（加利福尼亚大学洛杉矶分校）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Joint Institute for Regional Earth System Science and Engineering, University of California - Los Angeles（加利福尼亚大学洛杉矶分校区域地球系统科学与工程联合研究所）
本文基于 OPERA RTC-S1 数据训练无需标签的自监督 vision transformer，通过逐像素基线分布偏差检测地表扰动，并在三地灾害事件中取得优于现有方法的结果。
CLAIRE: A Dual Encoder Network with RIFT Loss and Phi-3 Small Language Model Based Interpretability for Cross-Modality Synthetic Aperture Radar and Optical Land Cover Segmentation
Department of Computer Science and Engineering, United International University（计算机科学与工程系，国际大学）；Faculty of Science and Information Technology, Charles Darwin University（科学与信息技术学院，查尔斯·达尔文大学）；School of Engineering and Energy , Murdoch University（工程与能源学院，默多尼大学）；Faculty of Science and Technology, Charles Darwin University（科学与技术学院，查尔斯·达尔文大学）
SARLO-80: Worldwide Slant SAR Language Optic Dataset 80cm
DEMR-ONERA – The French Aerospace Lab, Université Paris-Saclay（法国航空航天实验室DEMR-ONERA，巴黎-萨克雷大学）；DTIS-ONERA – The French Aerospace Lab, Université Paris-Saclay（法国航空航天实验室DTIS-ONERA，巴黎-萨克雷大学）；Hugging Face
A Deep Learning Approach to Estimate Canopy Height and Uncertainty by Integrating Seasonal Optical, SAR and Limited GEDI LiDAR Data over Northern Forests
MIMA: MAPPER-Induced Manifold Alignment for Semi-Supervised Fusion of Optical Image and Polarimetric SAR Data
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））；Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM)（慕尼黑工业大学（TUM）地球观测信号处理实验室（SiPEO））
Cloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions
Institute of Aerospace and System Engineering, National Taipei University of Technology, Taipei, Taiwan（航空航天工程研究所，台北科技大学，台北，台湾）；Department of Information Technology, Ho Chi Minh City University of Education, Ho Chi Minh City, Vietnam（信息技术系，胡志明市教育大学，胡志明市，越南）
Improved flood mapping for efficient policy design by fusion of Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and infrastructure exposed to floods
Lahore University of Management Sciences (LUMS)（拉合尔管理科学大学（LUMS））
RSTNet: Enhancing Small-Target Recognition in Noisy SAR Imagery via Robust Feature Learning and Distribution-Aware Regression
College of Mathematics and Information Science, Anyang Institute of Technology（数学与信息科学学院，安阳职业技术学院）；School of Computer Science and Information Engineering, Anyang Institute of Technology（计算机科学与信息工程学院，安阳职业技术学院）；College of Electronic Information and Optical Engineering, Nankai University（电子信息与光学工程学院，南开大学）；Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology（天津光电传感器与传感网络技术重点实验室）；Beijing Xiao Mi Mobile Software Co., Ltd.（北京小米移动软件有限公司）；Anyang Embedded Intelligent Robot Co., Ltd.（安阳嵌入式智能机器人有限公司）
M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data
University of Cambridge（剑桥大学）；Drexel University（德雷塞尔大学）；Universitat de València（瓦伦西亚大学）；European Space Agency（欧洲空间局）；University of Oxford（牛津大学）；Universidad de Antioquia（安蒂奥基亚大学）
PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification
Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572025, China（武汉理工大学三亚科学教育创新园）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China（武汉理工大学计算机科学与人工智能学院）；Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China（中国科学院海洋循环与波浪重点实验室）；Hubei Key Laboratory of Transportation Internet of Things, School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China（湖北省交通物联网重点实验室）；State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China（武汉理工大学航海技术与安全国家重点实验室）；College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai 201306, China（上海海洋大学海洋科学与生态学院）；School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China（郑州大学计算机与人工智能学院）；Rapid-Rich Object Search Lab, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798（南洋理工大学电子与电气工程学院）
Cross-modal learning for SAR target recognition using optical vision foundation models
School of Engineering, University of Edinburgh（爱丁堡大学工程学院）；Leonardo UK（英国莱昂纳多公司）；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）
Heterogeneous Feature Distillation Network for SAR Image Semantic Segmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
Prior-guided Fusion of Multimodal Features for Change Detection from Optical-SAR Images
Institute of Geospatial Information, Information Engineering University（地理信息研究所，信息工程大学）；Academy of Digital China (Fujian), Fuzhou University（数字中国研究院（福建），福州大学）；The School of Electronics and Communication Engineering, Sun Yat-sen University（电子与通信工程学院，中山大学）；The Department of Information Engineering and Computer Science, University of Trento（信息工程与计算机科学系，特伦托大学）
Physically Explainable CNN for SAR Image Classification
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））
SOMA-1M: A Large-Scale SAR-Optical Multi-resolution Alignment Dataset for Multi-Task Remote Sensing
School of Remote Sensing Information Engineering, Wuhan University（遥感信息工程学院，武汉大学）；Hubei LuoJia Laboratory（湖北珞珈实验室）；Technology Innovation Center for Collaborative Applications of Natural Resources Data in GBA, Ministry of Natural Resources（粤港澳大湾区自然资源数据协同应用技术创新中心，自然资源部）
Extending Dynamic World Surface Water Mapping to Sentinel-1 with AlphaEarth Embeddings
Pacific Northwest National Laboratory（太平洋西北国家实验室）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；Fujitsu Research of America（富士通美国研究院）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery
Universidad Nacional de Colombia（哥伦比亚国立大学）；European Space Agency（欧洲空间局）；Universitat de València（瓦伦西亚大学）；University of Cambridge（剑桥大学）；University of Oxford（牛津大学）；Universidad de Antioquia（安蒂奥基亚大学）
