Towards Super-Resolution SAR Tomography of Forest Areas using Deep Learning-Assisted Compressive Sensing

Cédric Léonard; Qi Zhang; Kun Qian; Yuanyuan Wang; Xiao Xiang Zhu

doi:https://doi.org/10.5194/egusphere-egu23-4296

[Back] [Session ITS1.14/CL5.8]

EGU23-4296, updated on 26 Jun 2024

https://doi.org/10.5194/egusphere-egu23-4296

EGU General Assembly 2023

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Towards Super-Resolution SAR Tomography of Forest Areas using Deep Learning-Assisted Compressive Sensing

Cédric Léonard^1,2, Qi Zhang¹, Kun Qian¹, Yuanyuan Wang¹, and Xiao Xiang Zhu

¹

Cédric Léonard et al.

¹Chair of Data Science in Earth Observation, Technical University of Munich (TUM), Munich, Germany
²Remote Sensing Technology Institute, German Aerospace Center (DLR), Weßling, Germany

How to cite: Léonard, C., Zhang, Q., Qian, K., Wang, Y., and Zhu, X. X.: Towards Super-Resolution SAR Tomography of Forest Areas using Deep Learning-Assisted Compressive Sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4296, https://doi.org/10.5194/egusphere-egu23-4296, 2023.

This abstract has been withdrawn on 03 Apr 2023.