TU2.R1.1

EMBEDDING ATTRIBUTE SCATTERING CENTER WITH CONVOLUTIONAL PROTOTYPE LEARNING FOR SAR OPEN-SET RECOGNITION

Xiayang Xiao, Zhuoxuan Li, Fudan University, China; Xiang Wu, Overall Design Institute of Hubei Aerospace Technology Research Institute, China; Haipeng Wang, Fudan University, China

Session:
TU2.R1: Object Detection and Recognition IX Oral

Track:
Data Analysis

Location:
Trianti

Presentation Time:
Tue, 9 Jul, 11:40 - 11:54

Session Co-Chairs:
Jade Guisiano, ISEP, École Polytechnique, UN Environment Programme and Maximilian Kleebauer, University of Kassel and Manuel Reese, Osnabrück University
Presentation
Discussion
Resources
No resources available.
Session TU2.R1
TU2.R1.1: EMBEDDING ATTRIBUTE SCATTERING CENTER WITH CONVOLUTIONAL PROTOTYPE LEARNING FOR SAR OPEN-SET RECOGNITION
Xiayang Xiao, Zhuoxuan Li, Fudan University, China; Xiang Wu, Overall Design Institute of Hubei Aerospace Technology Research Institute, China; Haipeng Wang, Fudan University, China
TU2.R1.2: Object detection models sensitivity & robustness to satellite-based adversarial attacks
Jade Guisiano, ISEP, École Polytechnique, UN Environment Programme, France; Domenico Barretta, University of Campania “Luigi Vanvitelli”, Italy; Éric Moulines, École polytechnique, France; Thomas Lauvaux, Université de Reims, France; Jérémie Sublime, Insitut Supérieur d’Électronique de Paris, France
TU2.R1.3: AN INTEGRATED APPROACH FOR LANDSCAPE ELEMENT DETECTION AND CHARACTERIZATION USING SENTINEL-2 AND ENMAP DATA
Manuel Reese, Björn Waske, Osnabrück University, Germany
TU2.R1.4: CROSS TEACHING BETWEEN SINGLE-SPECTRAL AND MULTI-SPECTRAL DETECTION TRANSFORMERS FOR REMOTE SENSING OBJECT DETECTION
Jiahe Zhu, Kaiyue Zhou, Huan Zhang, Shengjin Wang, Hongbing Ma, Tsinghua University, China
TU2.R1.5: Decoupled Multi-Teacher: Cross-modal Learning Enhanced Object Detection in SAR Imagery
Ruixiang Zhang, Yuxuan Wang, Haoyuan Li, Wuhan University, China; Pingping Huang, Inner Mongolia University of Technology, China; Wen Yang, Wuhan University, China
TU2.R1.6: Enhancing Wind Turbine Location Accuracy: A Deep Learning-Based Object Regression Approach for Validating Wind Turbine Geo-Coordinates
Maximilian Kleebauer, University of Kassel, Germany; Axel Braun, Fraunhofer Institute for Energy Economics and Energy System Technology, Germany; Daniel Horst, University of Kassel, Germany; Carsten Pape, Fraunhofer Institute for Energy Economics and Energy System Technology, Germany
TU2.R1.7: Non-cooperate Target Recognition via Proxy-based Feature Ensemble Supervised Contrastive Learning
Meng Lei, Yalong Lv, Yipeng Wang, Ying Zhang, University of Electronic Science and Technology of China, China
Resources
No resources available.