A deep residual network implementation for satellite-derived altimetry identification and classification

Jeffrey S. Perry; Amy L. Neuenschwander; Matthew J. Holwill; Lori A. Magruder

doi:10.1117/12.3021196

5 June 2024 A deep residual network implementation for satellite-derived altimetry identification and classification

Jeffrey S. Perry, Amy L. Neuenschwander, Matthew J. Holwill, Lori A. Magruder

Proceedings Volume 13049, Laser Radar Technology and Applications XXIX; 130490A (2024) https://doi.org/10.1117/12.3021196
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

ICESat-2's Advanced Terrain Laser Altimeter System (ATLAS) can penetrate water bodies, enabling accurate and detailed measurements of bathymetry in diverse aquatic environments. ATLAS's capabilities have made it a popular tool for understanding underwater topography and characteristics. In this paper, we present a deep residual classification network used to identify ICESat-2 bathymetry and water surface photons. The training data used to train the model was derived from both hand-labeled ICESat-2 groundtracks and from synthetic data produced by custom ICESat-2 ground track simulator software. This investigation was unique in that it used a very wide variety of ground tracks across the entire globe, and it also used several different metrics to summarize the classification performance.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jeffrey S. Perry, Amy L. Neuenschwander, Matthew J. Holwill, and Lori A. Magruder "A deep residual network implementation for satellite-derived altimetry identification and classification", Proc. SPIE 13049, Laser Radar Technology and Applications XXIX, 130490A (5 June 2024); https://doi.org/10.1117/12.3021196

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