Urban Surface Reconstruction in SAR Tomography by Graph-Cuts
Télécom ParisTech, Université Paris-Saclay（巴黎萨克雷大学电信学院）；Univ Lyon, UJM-Saint-Etienne（里昂大学，圣艾蒂安大学）；ONERA, The French Aerospace Laboratory（法国航空航天研究院）；Institute of Electronics and Telecommunications of Rennes (IETR), University of Rennes 1（雷恩第一大学雷恩电子与电信研究所）
SAR (Synthetic Aperture Radar) tomography reconstructs 3-D volumes from stacks of SAR images. High-resolution satellites such as TerraSAR-X provide images that can be combined to produce 3-D models. In urban areas, sparsity priors are generally enforced during the tomographic inversion process in order to retrieve the location of scatterers seen within a given radar resolution cell. However, such priors often miss parts of the urban surfaces. Those missing parts are typically regions of flat areas such as ground or rooftops. This paper introduces a surface segmentation algorithm based on the computation of the optimal cut in a flow network. This segmentation process can be included within the 3-D reconstruction framework in order to improve the recovery of urban surfaces. Illustrations on a TerraSAR-X tomographic dataset demonstrate the potential of the approach to produce a 3-D model of urban surfaces such as ground, façades and rooftops.
Urban Flood Mapping Using Satellite Synthetic Aperture Radar Data: A Review of Characteristics, Approaches and Datasets
Technical University of Munich（慕尼黑工业大学）；Vienna University of Technology（维也纳工业大学）；ETH Zurich（苏黎世联邦理工学院）；Wuhan University（武汉大学）；DiXplorer AG（4DiXplorer公司）；Luxembourg Institute of Science and Technology (LIST)（卢森堡科学技术研究院）
CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images
Remote Sensing Technology Institute, German Aerospace Center（德国航空航天中心遥感技术研究所）；Signal Processing in Earth Observation, Technical University of Munich（慕尼黑工业大学地球观测信号处理部门）
An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images
Department of Electrical, Computer and Biomedical Engineering, University of Pavia（电子、计算机与生物医学工程系，帕维亚大学）；Department of Engineering, University of Sannio（工程系，萨尼奥大学）
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）
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）
A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data
Department of Electrical, Computer and Biomedical Engineering, University of Pavia（电气、计算机与生物医学工程系，帕维亚大学）；Department of Engineering, University of Sannio（工程系，萨尼奥大学）；Department of Civil, Building and Environmental Engineering, Sapienza University of Rome（土木、建筑与环境工程系，罗马萨皮恩扎大学）；Φ sans-serif-Φ \mathsf{\Phi} sansserif_Φ -lab, European Space Agency（Φ实验室，欧洲航天局）
Reconstructing Building Height from Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network
The Chair of Data Science in Earth Observation, Technical University of Munich (TUM)（地球观测数据科学教授职位，慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；School of Engineering and Design, Technical University of Munich (TUM)（工程与设计学院，慕尼黑技术大学）
Multi-modal Uncertainty Robust Tree Cover Segmentation For High-Resolution Remote Sensing Images
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间智能信息处理国家重点实验室）；Department of Electrical and Computer Engineering, University of Delaware（电气与计算机工程系，德雷塞尔大学）；Swiss Federal Institute for Forest, Snow, and Landscape Research WSL（瑞士森林、雪和景观研究联邦 institute WSL）
