Smooth Hermean surface extraction by Region Growing from MESSENGER Laser Altimeter Data

The detailed and accurate shape of the terrain is necessary for some fields, such as geodetic analysis and geomorphology, which are concerned with the physical processes of a planet and its orbital attitudes. The Mercury Laser Altimeter (MLA) onboard the MESSENGER orbiter has performed successive altimetry measurements between 2011 and 2015. These measurements provide a global shape of the planet with approximately 3200 laser profiles, with the majority of these profiles covering the northern hemisphere due to MESSENGER's highly elliptical orbit (Cavanaugh et al., 2007).  A number of studies have sought to derive digital terrain models (DTMs) of Mercury using stereo-imaging techniques (Fassett, 2016; Manheim et al., 2022, Florinsky, 2018). Some have employed MLA data (Zuber et al., 2012; Preusker et al., 2017).  However, challenges remain due to discrepancies in the laser altimeter tracks, which are thought to be due to residual errors in spacecraft orbit, pointing, etc. Filtering these offsets provides an opportunity for further refinement and improvement of DTM generation methods for Mercury's surface.
The aim of this paper is to accurately extract terrain from MLA point clouds, particularly around the characteristic features of the northern hemisphere, at high resolutions. In this work, we propose an algorithm that follows a region-growing approach to extract the terrain. To identify the optimal surface solution in each local patch (Arghavanian, 2022), we apply machine learning methods and statistical parameters, starting from regions that are relatively easy to fit and then growing to the most challenging areas.   Additionally, users have the flexibility to add or remove constraints or fine-tune terrain-relevant and empirical parameters based on their specific topography. The final surface is obtained by global surface fitting, thereby filling any gaps in the data. To evaluate the performance of the proposed method, the results will be compared with previously produced DEMs.
References: 
Arghavanian A., 2022, Channel detection and tracking from LiDAR data in complicated terrain, PhD thesis, Middle East Technical University, Ankara, Turkey. 
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