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\begin{document}

\title{Exploiting the Redundancy in ICESat-2 Geolocated Photon Data (ATL03), a
Multiscale Data Reduction Approach}



\author[1]{Prashant Shekhar}%
\author[2]{Beata Csatho}%
\author[2]{Toni Schenk}%
\author[1]{Abani Patra}%
\affil[1]{Tufts University}%
\affil[2]{University at Buffalo}%


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  \date{May 05, 2024}


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\begin{abstract}
ICESat-2, the first photon counting satellite, maps the earth's surface
with unprecedented details and accuracy. However, the resulting photon
clouds, distributed as the ATL-03 geocoded photon product, are vast,
unstructured, and noisy datasets. The high density and four-dimensional
nature of the photon dataset (location and time), coupled with different
responses over different surfaces (e.g., ice, forest cover, water), pose
a unique and challenging problem regarding surface detection's overall
objective of intelligently reducing the data volume. Multiscale models
uncover hidden structures in data due to their ability to analyze the
underlying processes at multiple scales. Besides the traditional wisdom
of using multiple scales for improving local and global approximations,
in this work, we show their application as an intelligent sampling
mechanism for redundant and noisy datasets. Our proposed approach's
fundamental idea is the generation of data dependent and multiscale
basis functions and corresponding representative sparse representations,
which retain points essential for minimizing the error of
reconstruction. Thereby, points associated with rapid spatial change are
chosen, while those that are easily reconstructed using the smooth basis
functions are discarded. As the final output, the algorithm provides an
efficient sparse representation of the data that captures all relevant
features for modeling and prediction with quantified uncertainty. Our
presentation includes a detailed description of the algorithm and theory
as applied to process the ATL-03 geolocated photon product of the
ICESat-2 mission. We will demonstrate the efficiency of our approach by
examples of different ice sheet surfaces, including heavily crevassed
glaciers, that pose a challenge for currently used change detection
methods.%
\end{abstract}%



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