Deep Neural Network Based Automatic Grounding Line Delineation In DInsar Interferograms
Accurate identification of grounding lines is of immense importance for estimating the mass budgets of ocean-terminating ice sheets and glaciers of Antarctica and Greenland. In Differential Interferometric SAR (DInSAR) interferograms, human experts still largely manually digitize grounding lines. Th...
| Published in: | IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium |
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| Main Authors: | , , , |
| Format: | Conference Object |
| Language: | English |
| Published: |
IEEE
2023
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| Subjects: | |
| Online Access: | https://elib.dlr.de/199052/ https://elib.dlr.de/199052/1/Deep_Neural_Network_Based_Automatic_Grounding_Line_Delineation_In_Dinsar_Interferograms.pdf https://ieeexplore.ieee.org/document/10282372 |
| Summary: | Accurate identification of grounding lines is of immense importance for estimating the mass budgets of ocean-terminating ice sheets and glaciers of Antarctica and Greenland. In Differential Interferometric SAR (DInSAR) interferograms, human experts still largely manually digitize grounding lines. The time-consuming nature of this task makes it infeasible to produce timely, continent-wide grounding line mappings. This study employed a Deep Neural Network (DNN) to automate delineation. The Holistically-Nested Edge Detection (HED) network was trained in a supervised manner on features derived from interferometric phase, elevation data, ice velocity, tidal amplitude, atmospheric pressure and corresponding manual delineations. HED-generated lines achieved a median deviation of 209 m with a median absolute deviation of 153 m from manual delineations. The developed automatic pipeline demonstrates the potential for generating spatially and temporally dense mappings of the grounding line. |
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