The Comparison of Denoising Methods for Photon Counting Laser Altimeter Data

2019 ◽  
Author(s):  
Dan Ye ◽  
Huan Xie ◽  
Xiaohua Tong ◽  
Zhong Zhang ◽  
Ming Li
Keyword(s):  
Photon Counting ◽  
Altimeter Data ◽  
Laser Altimeter

A Method of Extracting High-Accuracy Elevation Control Points from ICESat-2 Altimetry Data

2021 ◽  
Vol 87 (11) ◽  
pp. 821-830
Author(s):  
Binbin Li ◽  
Huan Xie ◽  
Shijie Liu ◽  
Xiaohua Tong ◽  
Hong Tang ◽  
...  
Keyword(s):  
Large Scale ◽  
Comprehensive Evaluation ◽  
Photon Counting ◽  
High Accuracy ◽  
Airborne Lidar ◽  
Altimeter Data ◽  
Control Points ◽  
Laser Altimeter ◽  
Terrain Elevation ◽  
Along Track

Due to its high ranging accuracy, spaceborne laser altimetry technology can improve the accuracy of satellite stereo mapping without ground control points. In the past, full-waveform ICE, CLOUD, and Land Elevation Satellite (ICESat) laser altimeter data have been used as one of the main data sources for global elevation control. As a second-generation satellite, ICESat-2 is equipped with an altimeter using photon counting mode. This can further improve the application capability for stereo mapping because of the six laser beams with high along-track repetition frequency, which can provide more detailed ground contour descriptions. Previous studies have addressed how to extract high-accuracy elevation control points from ICESat data. However, these methods cannot be directly applied to ICESat-2 data because of the different modes of the laser altimeters. Therefore, in this paper, we propose a method using comprehensive evaluation labels that can extract high-accuracy elevation control points that meet the different level elevation accuracy requirements for large scale mapping from the ICESat-2 land-vegetation along-track product. The method was verified using two airborne lidar data sets. In flat, hilly, and mountainous areas, by using our method to extract the terrain elevation, the root-mean-square error of elevation control points decrease from 1.249–2.094 m, 2.237–3.225 m, and 2.791–4.822 m to 0.262–0.429 m, 0.484–0.596 m, and 0.611–1.003 m, respectively. The results show that the extraction elevations meet the required accuracy for large scale mapping.

scholarly journals Iterative Pointing Angle Calibration Method for the Spaceborne Photon-Counting Laser Altimeter Based on Small-Range Terrain Matching

2019 ◽  
Vol 11 (18) ◽  
pp. 2158
Author(s):  
Nan ◽  
Feng ◽  
Liu ◽  
Li
Keyword(s):  
Photon Counting ◽  
Calibration Method ◽  
High Repetition Rate ◽  
Search Method ◽  
Airborne Lidar ◽  
Altimeter Data ◽  
Small Range ◽  
Laser Altimeter ◽  
Ice Cloud ◽  
Terrain Matching

The satellite, Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) has been equipped with a new type of spaceborne laser altimeter, which has the benefits of having small footprints and a high repetition rate, and it can produce dense footprints on the ground. Focusing on the pointing angle calibration of this new spaceborne laser altimeter, this paper proposes a fast pointing angle calibration method using only a small range of terrain surveyed by airborne lidar. Based on the matching criterion of least elevation difference, an iterative pointing angle calibration method was proposed. In the experiment, the simulated photon-counting laser altimeter data and the Ice, Cloud and Land Elevation Satellite-2 data were used to verify the algorithm. The results show that when 1 km and 2.5 km lengths of track were used, the pointing angle error after calibration could be reduced to about 0.3 arc-seconds and less than 0.1 arc-seconds, respectively. Meanwhile, compared with the traditional pyramid search method, the proposed iterative pointing angle calibration method does not require well-designed parameters, which are important in the pyramid search method to balance calculation time and calibration result, and the iterative pointing angle calibration method could significantly reduce the calibration time to only about one-fifth of that of the pyramid search method.

scholarly journals Alignment determination of the Hayabusa2 laser altimeter (LIDAR)

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Hirotomo Noda ◽  
Hiroki Senshu ◽  
Koji Matsumoto ◽  
Noriyuki Namiki ◽  
Takahide Mizuno ◽  
...  
Keyword(s):  
Altimeter Data ◽  
Gravity Assist ◽  
Laser Altimeter ◽  
Trajectory Error ◽  
Flight Data ◽  
Camera Image ◽  
Operation Period ◽  
The Cross ◽  
Along Track

AbstractIn this study, we determined the alignment of the laser altimeter aboard Hayabusa2 with respect to the spacecraft using in-flight data. Since the laser altimeter data were used to estimate the trajectory of the Hayabusa2 spacecraft, the pointing direction of the altimeter needed to be accurately determined. The boresight direction of the receiving telescope was estimated by comparing elevations of the laser altimeter data and camera images, and was confirmed by identifying prominent terrains of other datasets. The estimated boresight direction obtained by the laser link experiment in the winter of 2015, during the Earth’s gravity assist operation period, differed from the direction estimated in this study, which fell on another part of the candidate direction; this was not selected in a previous study. Assuming that the uncertainty of alignment determination of the laser altimeter boresight was 4.6 pixels in the camera image, the trajectory error of the spacecraft in the cross- and/or along-track directions was determined to be 0.4, 2.1, or 8.6 m for altitudes of 1, 5, or 20 km, respectively.

scholarly journals Measurement of ice-sheet topography using satellite-radar interferometry

1996 ◽  
Vol 42 (140) ◽  
pp. 10-22 ◽  
Author(s):  
Ian Joughin ◽  
Dale Winebrenner ◽  
Mark Fahnestock ◽  
Ron Kwok ◽  
William Krabill
Keyword(s):  
Ice Sheets ◽  
Ice Sheet ◽  
Surface Displacement ◽  
Horizontal Resolution ◽  
Radar Interferometry ◽  
Altimeter Data ◽  
Laser Altimeter ◽  
Digital Elevation ◽  
Satellite Radar ◽  
Ice Sheet Topography

AbstractDetailed digital elevation models (DEMs) do not exist for much of the Greenland and Antartic ice sheets. Radar altimetry is at present the primary, in many cases the only, source of topographic data over the ice sheets, but the horizontal resolution of such data is coarse. Satellite-radar interferometry uses the phase difference between pairs of synthetic aperture radar (SAR) images to measure both ice-sheet topography and surface displacement. We have applied this technique using ERS-1 SAR data to make detailed (i.e. 80 m horizontal resolution) maps of surface topography in a 100 km by 300 km strip in West Greenland, extending northward from just above Jakobshavns Isbræ. Comparison with а 76 km long line of airborne laser-altimeter data shows that We have achieved a relative accuracy of 2.5 m along the profile. These observations provide a detailed view of dynamically Supported topography near the margin of an ice sheet. In the final section We compare our estimate of topography with phase contours due to motion, and confirm our earlier analysis concerning vertical ice-sheet motion and complexity in ERS-1 SAR interferograms.

Global Mapping and Analysis of the Structural Features in the Northern Smooth Plains of Mercury

2021 ◽  
Author(s):  
marco cardinale ◽  
Gaetano Di Achille ◽  
David A.Vaz
Keyword(s):  
Imaging System ◽  
Structural Features ◽  
Planetary Science ◽  
Altimeter Data ◽  
Normal Faults ◽  
Laser Altimeter ◽  
Volcanic Material ◽  
Geological Map ◽  
Dual Imaging

<p>Orbital data from the Messenger spacecraft (1) reveal that part of the Mercury surface is covered by smooth plains, which are interpreted to be flood volcanic material across the planetary surface (2). In this work, we present a detailed geo-structural map of the northern smooth plains between<span class="Apple-converted-space">  </span>latitudes 29°N and 65°N. Our 1:100.000-scale map is obtained semi-automatically, using an algorithm to map all scarps from a DEM (3,4) followed by visual inspection and classification in ArcGIS. We created a DEM<span class="Apple-converted-space">  </span>using the raw MLA (Mercury Laser Altimeter) data (1) ,with 500 m/pix, and we used the Mercury Messenger MDIS (Mercury Dual Imaging System) (1,2) base map with 166m per pixel for the classification stage. With this approach, we mapped and characterized 51664 features on Mercury, creating a database with several morphometric attributes (e.g. length, azimuth, scarp height) which we will use to study the tectonic evolution of the smooth plains.<span class="Apple-converted-space"> </span></p> <p>In this way, we classified wrinkle ridges’s scarps, ghost craters, rim craters and central peaks. The morphometric parameters of the wrinkle ridges will<span class="Apple-converted-space">  </span>be quantitatively analyzed, in order to characterizer the possible tectonic process that could have formed them.</p> <p>This map can be considered an enhancement for the north pole of the global geological map of Mercury (1, 5).</p> <p> </p> <p>References</p> <ul> <li>Hawkins, S. E., III, et al. (2007), The Mercury Dual Imaging System on the MESSENGER spacecraft, Space Sci. Rev., 131, 247–338..<span class="Apple-converted-space"> </span></li> <li>Denevi, B. W., et al. (2013), The distribution and origin of smooth plains on Mercury, J. Geophys. Res. Planets, 118, 891–907, doi:10.1002/jgre.20075.</li> <li>Alegre Vaz, D. (2011). Analysis of a Thaumasia Planum rift through automatic mapping and strain characterization of normal faults. Planetary and Space Science, 59(11-12), 1210–1221. doi:10.1016/j.pss.2010.07.008 .</li> <li>Vaz, D. A., Spagnuolo, M. G., & Silvestro, S. (2014). Morphometric and geometric characterization of normal faults on Mars. Earth and Planetary Science Letters, 401, 83–94. doi:10.1016/j.epsl.2014.05.022.</li> <li>Kinczyk, M. J., Prockter, L., Byrne, P., Denevi, B., Buczkowski, D., Ostrach, L., & Miller, E. (2019, September). The First Global Geological Map of Mercury. In <em>EPSC-DPS Joint Meeting 2019</em> (Vol. 2019, pp. EPSC-DPS2019).</li> </ul>

Error identification in orbital laser altimeter data by machine learning

2021 ◽  
Author(s):  
Oliver Stenzel ◽  
Robin Thor ◽  
Martin Hilchenbach
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Space Science ◽  
Altimeter Data ◽  
Laser Altimetry ◽  
Error Identification ◽  
Laser Altimeter ◽  
Data Set ◽  
Baseline Design ◽  
Solar System Bodies

<p>Orbital Laser altimeters deliver a plethora of data that is used to map planetary surfaces [1] and to understand interiors of solar system bodies [2]. Accuracy and precision of laser altimetry measurements depend on the knowledge of spacecraft position and pointing and on the instrument. Both are important for the retrieval of tidal parameters. In order to assess the quality of the altimeter retrievals, we are training and implementing an artificial neural network (ANN) to identify and exclude scans from analysis which yield erroneous data. The implementation is based on the PyTorch framework [3]. We are presenting our results for the MESSENGER Mercury Laser Altimeter (MLA) data set [4], but also in view of future analysis of the BepiColombo Laser Altimeter (BELA) data, which will arrive in orbit around Mercury in 2025 on board the Mercury Planetary Orbiter [5,6]. We further explore conventional methods of error identification and compare these with the machine learning results. Short periods of large residuals or large variation of residuals are identified and used to detect erroneous measurements. Furthermore, long-period systematics, such as those caused by slow variations in instrument pointing, can be modelled by including additional parameters.<br>[1] Zuber, Maria T., David E. Smith, Roger J. Phillips, Sean C. Solomon, Gregory A. Neumann, Steven A. Hauck, Stanton J. Peale, et al. ‘Topography of the Northern Hemisphere of Mercury from MESSENGER Laser Altimetry’. Science 336, no. 6078 (13 April 2012): 217–20. https://doi.org/10.1126/science.1218805.<br>[2] Thor, Robin N., Reinald Kallenbach, Ulrich R. Christensen, Philipp Gläser, Alexander Stark, Gregor Steinbrügge, and Jürgen Oberst. ‘Determination of the Lunar Body Tide from Global Laser Altimetry Data’. Journal of Geodesy 95, no. 1 (23 December 2020): 4. https://doi.org/10.1007/s00190-020-01455-8.<br>[3] Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. ‘PyTorch: An Imperative Style, High-Performance Deep Learning Library’. Advances in Neural Information Processing Systems 32 (2019): 8026–37.<br>[4] Cavanaugh, John F., James C. Smith, Xiaoli Sun, Arlin E. Bartels, Luis Ramos-Izquierdo, Danny J. Krebs, Jan F. McGarry, et al. ‘The Mercury Laser Altimeter Instrument for the MESSENGER Mission’. Space Science Reviews 131, no. 1 (1 August 2007): 451–79. https://doi.org/10.1007/s11214-007-9273-4.<br>[5] Thomas, N., T. Spohn, J. -P. Barriot, W. Benz, G. Beutler, U. Christensen, V. Dehant, et al. ‘The BepiColombo Laser Altimeter (BELA): Concept and Baseline Design’. Planetary and Space Science 55, no. 10 (1 July 2007): 1398–1413. https://doi.org/10.1016/j.pss.2007.03.003.<br>[6] Benkhoff, Johannes, Jan van Casteren, Hajime Hayakawa, Masaki Fujimoto, Harri Laakso, Mauro Novara, Paolo Ferri, Helen R. Middleton, and Ruth Ziethe. ‘BepiColombo—Comprehensive Exploration of Mercury: Mission Overview and Science Goals’. Planetary and Space Science, Comprehensive Science Investigations of Mercury: The scientific goals of the joint ESA/JAXA mission BepiColombo, 58, no. 1 (1 January 2010): 2–20. https://doi.org/10.1016/j.pss.2009.09.020.</p>

A Digital Terrain Modeling Method in Urban Areas by the ICESat-2 (Generating precise terrain surface profiles from photon-counting technology)

2021 ◽  
Vol 87 (4) ◽  
pp. 237-248
Author(s):  
Nahed Osama ◽  
Bisheng Yang ◽  
Yue Ma ◽  
Mohamed Freeshah
Keyword(s):  
Urban Areas ◽  
Spatial Clustering ◽  
Photon Counting ◽  
Ground Surface ◽  
Ground Truth ◽  
Surface Model ◽  
Laser Altimeter ◽  
Ground Truth Data ◽  
Urban Scenes ◽  
Terrain Surface

The ICE, Cloud and land Elevation Satellite-2 (ICES at-2) can provide new measurements of the Earth's elevations through photon-counting technology. Most research has focused on extracting the ground and the canopy photons in vegetated areas. Yet the extraction of the ground photons from urban areas, where the vegetation is mixed with artificial constructions, has not been fully investigated. This article proposes a new method to estimate the ground surface elevations in urban areas. The ICES at-2 signal photons were detected by the improved Density-Based Spatial Clustering of Applications with Noise algorithm and the Advanced Topographic Laser Altimeter System algorithm. The Advanced Land Observing Satellite-1 PALSAR –derived digital surface model has been utilized to separate the terrain surface from the ICES at-2 data. A set of ground-truth data was used to evaluate the accuracy of these two methods, and the achieved accuracy was up to 2.7 cm, which makes our method effective and accurate in determining the ground elevation in urban scenes.

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