TU3.M4.1
A Generative Approach for Self-Supervised TomoSAR Multidimensional Imaging
Liang Liu, Tianjiao Zeng, Mou Wang, Jun Shi, Shunjun Wei, Xiaoling Zhang, Xu Zhan, University of Electronic Science and Technology of China, China
Session:
TU3.M4: Deep Learning for Multi-channel SAR processing: Current State of Art and Future Trends in the Application of Deep Learning for 3D Reconstruction III Oral
Track:
Community-Contributed Sessions
Location:
Room M4
Presentation Time:
Tuesday, 5 August, 13:15 - 13:30
Session Co-Chairs:
Sergio Vitale, University of Naples Parthenope and Hossein Aghababaei, University of Twente
Presentation
Discussion
Resources
No resources available.
Session TU3.M4
TU3.M4.1: A Generative Approach for Self-Supervised TomoSAR Multidimensional Imaging
Liang Liu, Tianjiao Zeng, Mou Wang, Jun Shi, Shunjun Wei, Xiaoling Zhang, Xu Zhan, University of Electronic Science and Technology of China, China
TU3.M4.2: A Wavelet-based Generative and Adversarial Diffusion Model for Forest Height Estimation
Qi Zhang, Yuanyuan Wang, Xiao Xiang Zhu, Technical University of Munich, Germany
TU3.M4.3: HYBRID APPROACH OF CONVOLUTIONAL AND GRAPH CONVOLUTIONAL NETWORKS WITH PIXEL- AND SUPERPIXEL-LEVEL FEATURE FUSION AND ATTENTION MECHANISM FOR SAR IMAGE CLASSIFICATION
Eno Peter, Li Minn Ang, Sanjeev Srivastava, Kah Phooi Seng, University of Sunshine Coast, Australia
TU3.M4.4: SAR-NODE: SAR DESPECKLING USING NEURAL ORDINARY DIFFERENTIAL EQUATIONS
Ziqing Ma, Yao Li, Jiebao Sun, Zhichang Guo, Dazhi Zhang, Chang Yang, Boying Wu, Harbin Institute of Technology, China
Resources
No resources available.