Radar penetration of TanDEM-X on glaciers & ice caps SAR tomography for 3D imaging of snow and firn structures
The radar signal can penetrate into snow, firn, and ice bodies depending on liquid water content and internal structure of the porous medium as well as radar imaging parameters. Consequently, the interferometric phase center of the backscattered radar signal is located at a certain depth below the actual topographic surface, leading to a bias between the glacier surface and the estimated one from SAR interferometry (Rott et al 2021, Rizzoli et al 2017). Radar penetration is still a large source of uncertainty in estimates of glacier volume and mass change from bi-static SAR mission data like bi-static TanDEM-X or the upcoming HRWS MirrorSAR (Braun et al. 2019, Huber et al. 2020).
The uncertainty is highest in presence of dry snow, not affected by melting phenomena as in the accumulation area of high mountain glaciers or polar glaciers and ice caps outside the large ice sheets. Within this sub-project, we aim at estimating radar penetration by deploying a deep learning architecture using input data from SAR imagery like coherence, backscatter, frequency and geometric baseline as well as a terrain model. As reference data for training deep learning networks in a supervised manner, we will utilize the height difference between precise laser altimetric measurements (ICESat-2, Operation IceBridge, ka-band data from JPL UAVSAR system) for the surface and radar DEMs for the location of the mean interferometric phase center.
Methodologically, we suggest the use of a deep convolutional network to estimate the altimetry, phrasing it as a regression problem. In particular, we propose the adoption of AdaIn-based Tunable CycleGAN (Gu 2020). We believe the additional CycleGAN constraints in combination with the switchable adaptive instance normalization will serve as a valuable regularizer. Making the CycleGAN tunable and hence, omitting one generator from the original CycleGAN approach, leads to a significant reduction in memory requirements and a more stable training process even on small datasets. Additionally, we can incorporate other data such as the local incident angle as well as climatological information as conditional input for the discriminators, since both variables can significantly influence the interferometric SAR phase center depth below the actual surface. This SP is linked to SP 1.2 with a joint airborne survey as well as to SP 2.3 and SP 3.1.
For specific information on the sub-project please contact: Dr.-Ing. Paola Rizzoli, Münchener Straße 20, 82234 Weßling, T: 08153-28-1785, Paola.Rizzoli@dlr.de, https://www.dlr.de/hr/desktopdefault.aspx/tabid-2328/3447_read-46107
Co-PIs: M. Braun (FAU Geography), A. Maier (FAU Informatics), P. Millilo (Univ. Houston)
I am a Ph.D. student both at the German Aerospace Center (DLR) and the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), being supervised by Prof. Matthias Braun (FAU) and Dr. Paola Rizzoli (DLR).
I have a bachelor’s in Electrical Engineering and a master’s in Telecommunications, both from universities in Brazil (where I came from!). The goal of my research was to develop methods for target detection and pattern recognition in synthetic aperture radar (SAR) images, mainly in military applications. After I received my master’s degree (March/2021) and seeking to work in a field in which I could see my work impacting people’s lives, I worked as a data scientist for a startup in Brazil, tackling problems in the agribusiness market. The company was heavily driven by how to improve the sustainability of medium- and large-sized companies and farmers in agriculture, which was a great learning experience and provided a lot of personal growth.
In this scenario, I heard about the IDP M³OCCA project and its ambition to be an interdisciplinary and international environment aiming at understanding and solving problems that can truly impact society. I am glad to be part of the project and thrilled to continue my research career in such a place.
Subproject 2.2
Radar penetration of TanDEM-X on glaciers & ice caps SAR tomography for 3D imaging of snow and firn structures
The radar signal can penetrate into snow, firn, and ice bodies depending on liquid water content and internal structure of the porous medium as well as radar imaging parameters. Consequently, the interferometric phase center of the backscattered radar signal is located at a certain depth below the actual topographic surface, leading to a bias between the glacier surface and the estimated one from SAR interferometry (Rott et al 2021, Rizzoli et al 2017). Radar penetration is still a large source of uncertainty in estimates of glacier volume and mass change from bi-static SAR mission data like bi-static TanDEM-X or the upcoming HRWS MirrorSAR (Braun et al. 2019, Huber et al. 2020).
The uncertainty is highest in presence of dry snow, not affected by melting phenomena as in the accumulation area of high mountain glaciers or polar glaciers and ice caps outside the large ice sheets. Within this sub-project, we aim at estimating radar penetration by deploying a deep learning architecture using input data from SAR imagery like coherence, backscatter, frequency and geometric baseline as well as a terrain model. As reference data for training deep learning networks in a supervised manner, we will utilize the height difference between precise laser altimetric measurements (ICESat-2, Operation IceBridge, ka-band data from JPL UAVSAR system) for the surface and radar DEMs for the location of the mean interferometric phase center.
Methodologically, we suggest the use of a deep convolutional network to estimate the altimetry, phrasing it as a regression problem. In particular, we propose the adoption of AdaIn-based Tunable CycleGAN (Gu 2020). We believe the additional CycleGAN constraints in combination with the switchable adaptive instance normalization will serve as a valuable regularizer. Making the CycleGAN tunable and hence, omitting one generator from the original CycleGAN approach, leads to a significant reduction in memory requirements and a more stable training process even on small datasets. Additionally, we can incorporate other data such as the local incident angle as well as climatological information as conditional input for the discriminators, since both variables can significantly influence the interferometric SAR phase center depth below the actual surface. This SP is linked to SP 1.2 with a joint airborne survey as well as to SP 2.3 and SP 3.1.
For specific information on the sub-project please contact: Dr.-Ing. Paola Rizzoli, Münchener Straße 20, 82234 Weßling, T: 08153-28-1785, Paola.Rizzoli@dlr.de, https://www.dlr.de/hr/desktopdefault.aspx/tabid-2328/3447_read-46107
Co-PIs: M. Braun (FAU Geography), A. Maier (FAU Informatics), P. Millilo (Univ. Houston)
Get to know our project affiliated PhD students
Alexandre Becker
alexandre.campos@dlr.de
I am a Ph.D. student both at the German Aerospace Center (DLR) and the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), being supervised by Prof. Matthias Braun (FAU) and Dr. Paola Rizzoli (DLR).
I have a bachelor’s in Electrical Engineering and a master’s in Telecommunications, both from universities in Brazil (where I came from!). The goal of my research was to develop methods for target detection and pattern recognition in synthetic aperture radar (SAR) images, mainly in military applications. After I received my master’s degree (March/2021) and seeking to work in a field in which I could see my work impacting people’s lives, I worked as a data scientist for a startup in Brazil, tackling problems in the agribusiness market. The company was heavily driven by how to improve the sustainability of medium- and large-sized companies and farmers in agriculture, which was a great learning experience and provided a lot of personal growth.
In this scenario, I heard about the IDP M³OCCA project and its ambition to be an interdisciplinary and international environment aiming at understanding and solving problems that can truly impact society. I am glad to be part of the project and thrilled to continue my research career in such a place.