scholarly journals ANALYSIS OF INFLUENCING FACTORS OF CURVE MATCHING BASED GEOMETRIC CALIBRATION FOR ZY3-02 ALTIMETER DATA

2019 ◽  
Vol IV-2/W7 ◽  
pp. 221-226
Author(s):  
M. Zhou ◽  
L. S. Chen ◽  
J. H. Wang ◽  
G. E. Teng ◽  
C. R. Li ◽  
...  
Keyword(s):  
Control Point ◽  
Altimeter Data ◽  
Curve Matching ◽  
Altimetry Data ◽  
Laser Altimetry ◽  
Step Size ◽  
Laser Altimeter ◽  
Geometric Calibration ◽  
Calibration Methods ◽  
Initial Control

<p><strong>Abstract.</strong> High-precision on-orbit geometric calibration of spaceborne laser altimetry data is essential to its effective applications. Firstly, the existing calibration methods for laser altimeter data are analyzed. Then, a geometric calibration method based on curve matching is proposed. Compared to the existing methods, the proposed method does not rely on ground calibration field. Thus, it is efficiency in expense and time. Notably, three factors, i.e. matching method, initial control point selection and the step size of matching step, which significantly affect the results of calibration are analyzed respectively. The analysis was validated based on the original laser altimetry data obtained by ZY3-02 satellite. According to the results, the following conclusions can be drawn preliminarily: (1) Both the correlation coefficient maximum (COR) criterion and the mean square error minimum (MSD) criterion in the curve matching can be used to correct the systematic error in altimetry data. (2) The initial control points of the selected track should have a significant change trend and the slope within the laser footprints should be less than 15&amp;deg;. (3) Current experimental data show that the best step size for matching search is 10&amp;thinsp;m. The relevant conclusions can provide reference for the research of geometrical calibration and data processing of the same type of laser altimetry satellite.</p>

scholarly journals POINTING ANGLE CALIBRATION OF ZY3-02 SATELLITE LASER ALTIMETER USING TERRAIN MATCHING

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 205-210 ◽  
Author(s):  
G. Li ◽  
X. Tang ◽  
X. Gao ◽  
J. P. Huang ◽  
J. Chen ◽  
...  
Keyword(s):  
Altimetry Data ◽  
Laser Altimetry ◽  
Angle Error ◽  
Laser Altimeter ◽  
Geometric Calibration ◽  
Pointing Error ◽  
The Public ◽  
Ice Cloud ◽  
Laser Data ◽  
Terrain Matching

After GLAS (Geo-science Laser Altimeter System) loaded on the ICESat (Ice Cloud and land Elevation Satellite), satellite laser altimeter attracts more and more attention. ZY3-02 equipped with the Chinese first satellite laser altimeter has been successfully launched on 30<sup>th</sup> May, 2016. The geometric calibration is an important step for the laser data processing and application. The method to calculate the laser pointing angle error based on existed reference terrain data is proposed in this paper. The public version terrain data, such as 90m-SRTM and 30m-AW3D30, can be used to estimate the pointing angle of laser altimeter. The GLAS data with simulated pointing error and actual ZY3-02 laser altimetry data is experimented to validate the algorithm. The conclusion will be useful for the future domestic satellite laser altimeter.

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

&lt;p&gt;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.&lt;br&gt;[1] Zuber, Maria T., David E. Smith, Roger J. Phillips, Sean C. Solomon, Gregory A. Neumann, Steven A. Hauck, Stanton J. Peale, et al. &amp;#8216;Topography of the Northern Hemisphere of Mercury from MESSENGER Laser Altimetry&amp;#8217;. Science 336, no. 6078 (13 April 2012): 217&amp;#8211;20. https://doi.org/10.1126/science.1218805.&lt;br&gt;[2] Thor, Robin N., Reinald Kallenbach, Ulrich R. Christensen, Philipp Gl&amp;#228;ser, Alexander Stark, Gregor Steinbr&amp;#252;gge, and J&amp;#252;rgen Oberst. &amp;#8216;Determination of the Lunar Body Tide from Global Laser Altimetry Data&amp;#8217;. Journal of Geodesy 95, no. 1 (23 December 2020): 4. https://doi.org/10.1007/s00190-020-01455-8.&lt;br&gt;[3] Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. &amp;#8216;PyTorch: An Imperative Style, High-Performance Deep Learning Library&amp;#8217;. Advances in Neural Information Processing Systems 32 (2019): 8026&amp;#8211;37.&lt;br&gt;[4] Cavanaugh, John F., James C. Smith, Xiaoli Sun, Arlin E. Bartels, Luis Ramos-Izquierdo, Danny J. Krebs, Jan F. McGarry, et al. &amp;#8216;The Mercury Laser Altimeter Instrument for the MESSENGER Mission&amp;#8217;. Space Science Reviews 131, no. 1 (1 August 2007): 451&amp;#8211;79. https://doi.org/10.1007/s11214-007-9273-4.&lt;br&gt;[5] Thomas, N., T. Spohn, J. -P. Barriot, W. Benz, G. Beutler, U. Christensen, V. Dehant, et al. &amp;#8216;The BepiColombo Laser Altimeter (BELA): Concept and Baseline Design&amp;#8217;. Planetary and Space Science 55, no. 10 (1 July 2007): 1398&amp;#8211;1413. https://doi.org/10.1016/j.pss.2007.03.003.&lt;br&gt;[6] Benkhoff, Johannes, Jan van Casteren, Hajime Hayakawa, Masaki Fujimoto, Harri Laakso, Mauro Novara, Paolo Ferri, Helen R. Middleton, and Ruth Ziethe. &amp;#8216;BepiColombo&amp;#8212;Comprehensive Exploration of Mercury: Mission Overview and Science Goals&amp;#8217;. 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&amp;#8211;20. https://doi.org/10.1016/j.pss.2009.09.020.&lt;/p&gt;

scholarly journals Interannual changes of the floating ice shelf of Petermann Gletscher, North Greenland, from 2000 to 2012

2014 ◽  
Vol 60 (221) ◽  
pp. 489-499 ◽  
Author(s):  
Andreas Münchow ◽  
Laurie Padman ◽  
Helen A. Fricker
Keyword(s):  
Mass Loss ◽  
Three Dimensional ◽  
Greenland Ice Sheet ◽  
Altimeter Data ◽  
Ice Shelf ◽  
Laser Altimetry ◽  
Laser Altimeter ◽  
Airborne Laser ◽  
Tracking Surface ◽  
Basal Melting

AbstractPetermann Gletscher, northwest Greenland, drains 4% of the Greenland ice sheet into Nares Strait. Its floating ice shelf retreated from 81 to 48 km in length during two large calving events in 2010 and 2012. We document changes in the three-dimensional ice-shelf structure from 2000 to 2012, using repeated tracks of airborne laser altimetry and ice radio-echo sounding, ICESat laser altimetry and MODIS visible imagery. The recent ice-shelf velocity, measured by tracking surface features between flights in 2010 and 2011, is ~1.25 km a−1, ~15–30% faster than estimates made before 2010. The steady- state along-flow ice divergence represents 6.3 Gta−1 mass loss through basal melting (~5Gta−1) and surface melting and sublimation (~1.0Gta−1). Airborne laser altimeter data reveal thinning, both along a thin central channel and on the thicker ambient ice shelf. From 2007 to 2010 the ice shelf thinned by ~5 m a−1, which represents a non-steady mass loss of ~4.1 Gta−1. We suggest that thinning in the basal channels structurally weakened the ice shelf and may have played a role in the recent calving events.

scholarly journals IMPROVE THE ZY-3 HEIGHT ACCURACY USING ICESAT/GLAS LASER ALTIMETER DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 37-42
Author(s):  
Guoyuan Li ◽  
Xinming Tang ◽  
Xiaoming Gao ◽  
Chongyang Zhang ◽  
Tao Li
Keyword(s):  
High Resolution ◽  
Bundle Adjustment ◽  
Experimental Result ◽  
Altimeter Data ◽  
Surface Model ◽  
Laser Altimetry ◽  
Laser Altimeter ◽  
Ice Cloud ◽  
Elevation Data ◽  
Polar Ice Sheets

ZY-3 is the first civilian high resolution stereo mapping satellite, which has been launched on 9th, Jan, 2012. The aim of ZY-3 satellite is to obtain high resolution stereo images and support the 1:50000 scale national surveying and mapping. Although ZY-3 has very high accuracy for direct geo-locations without GCPs (Ground Control Points), use of some GCPs is still indispensible for high precise stereo mapping. The GLAS (Geo-science Laser Altimetry System) loaded on the ICESat (Ice Cloud and land Elevation Satellite), which is the first laser altimetry satellite for earth observation. GLAS has played an important role in the monitoring of polar ice sheets, the measuring of land topography and vegetation canopy heights after launched in 2003. Although GLAS has ended in 2009, the derived elevation dataset still can be used after selection by some criteria. <br><br> In this paper, the ICESat/GLAS laser altimeter data is used as height reference data to improve the ZY-3 height accuracy. A selection method is proposed to obtain high precision GLAS elevation data. Two strategies to improve the ZY-3 height accuracy are introduced. One is the conventional bundle adjustment based on RFM and bias-compensated model, in which the GLAS footprint data is viewed as height control. The second is to correct the DSM (Digital Surface Model) straightly by simple block adjustment, and the DSM is derived from the ZY-3 stereo imaging after freedom adjustment and dense image matching. The experimental result demonstrates that the height accuracy of ZY-3 without other GCPs can be improved to 3.0 meter after adding GLAS elevation data. What’s more, the comparison of the accuracy and efficiency between the two strategies is implemented for application.

scholarly journals IMPROVE THE ZY-3 HEIGHT ACCURACY USING ICESAT/GLAS LASER ALTIMETER DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 37-42 ◽  
Author(s):  
Guoyuan Li ◽  
Xinming Tang ◽  
Xiaoming Gao ◽  
Chongyang Zhang ◽  
Tao Li
Keyword(s):  
High Resolution ◽  
Bundle Adjustment ◽  
Experimental Result ◽  
Altimeter Data ◽  
Surface Model ◽  
Laser Altimetry ◽  
Laser Altimeter ◽  
Ice Cloud ◽  
Elevation Data ◽  
Polar Ice Sheets

ZY-3 is the first civilian high resolution stereo mapping satellite, which has been launched on 9th, Jan, 2012. The aim of ZY-3 satellite is to obtain high resolution stereo images and support the 1:50000 scale national surveying and mapping. Although ZY-3 has very high accuracy for direct geo-locations without GCPs (Ground Control Points), use of some GCPs is still indispensible for high precise stereo mapping. The GLAS (Geo-science Laser Altimetry System) loaded on the ICESat (Ice Cloud and land Elevation Satellite), which is the first laser altimetry satellite for earth observation. GLAS has played an important role in the monitoring of polar ice sheets, the measuring of land topography and vegetation canopy heights after launched in 2003. Although GLAS has ended in 2009, the derived elevation dataset still can be used after selection by some criteria. &lt;br&gt;&lt;br&gt; In this paper, the ICESat/GLAS laser altimeter data is used as height reference data to improve the ZY-3 height accuracy. A selection method is proposed to obtain high precision GLAS elevation data. Two strategies to improve the ZY-3 height accuracy are introduced. One is the conventional bundle adjustment based on RFM and bias-compensated model, in which the GLAS footprint data is viewed as height control. The second is to correct the DSM (Digital Surface Model) straightly by simple block adjustment, and the DSM is derived from the ZY-3 stereo imaging after freedom adjustment and dense image matching. The experimental result demonstrates that the height accuracy of ZY-3 without other GCPs can be improved to 3.0 meter after adding GLAS elevation data. What’s more, the comparison of the accuracy and efficiency between the two strategies is implemented for application.

Towards machine learning assisted error identification in orbital laser altimetry for tides derivation

2021 ◽  
Author(s):  
Oliver Stenzel ◽  
Martin Hilchenbach
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Training Dataset ◽  
Altimeter Data ◽  
Laser Altimetry ◽  
Linear Network ◽  
Error Identification ◽  
Laser Altimeter ◽  
Convolutional Network ◽  
The Impact

&lt;p&gt;Laser altimetry experiments on the NASA MESSENGER mission [1], and on the currently on cruise ESA/JAXA BepiColombo Mission [2,3] did and are going to yield, respectively, a plethora of range measurements of the surface of Mercury. Orbital laser altimetry can be used to derive tidal parameters, which can in turn be used to infer properties of a body&amp;#8217;s interior [4,5]. The derivation of tidal parameters requires large datasets of precise and accurate measurements. Errors as well as outliers can degrade the quality of the computed tidal parameters. While many outliers can be filtered though conventional automated processes, other errors could only be identified by human supervision. In the face of the amount of data involved, systematic user interaction at the error identification step becomes unpractical. A neural network trained with user expertise could help spotting outliers and errors and would improve the derived parameters in accuracy and precession. We started developing a neural network based on the pytorch framework[6] and compared the performance with a small training dataset form the MESSENGER Laser Altimeter (MLA) for a linear and a convolutional network. The results were much in favour of the linear network [7]. In this presentation we explore the reasons behind bad convolutional network performance with extended training and test datasets. We are going to show our results for the filtered datasets and the impact this has on the derived tidal parameters. The filtering with an artificial neural network might be useful for other applications, as well.&lt;/p&gt; &lt;p&gt;1. Cavanaugh, J. F. et al. The Mercury Laser Altimeter Instrument for the MESSENGER Mission. Space Sci Rev 131, 451&amp;#8211;479 (2007).&lt;/p&gt; &lt;p&gt;2. Benkhoff, J. et al. BepiColombo&amp;#8212;Comprehensive exploration of Mercury: Mission overview and science goals. Planetary and Space Science 58, 2&amp;#8211;20 (2010).&lt;/p&gt; &lt;p&gt;3. Thomas, N. et al. The BepiColombo Laser Altimeter. Space Sci Rev 217, 25 (2021).&lt;/p&gt; &lt;p&gt;4. Thor, R. N. et al. Determination of the lunar body tide from global laser altimetry data. J Geod 95, 4 (2021).&lt;/p&gt; &lt;p&gt;5. Thor, R. N. et al. Prospects for measuring Mercury&amp;#8217;s tidal Love number h2 with the BepiColombo Laser Altimeter. A&amp;A 633, A85 (2020).&lt;/p&gt; &lt;p&gt;6. Paszke, A., et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library, In: Advances in Neural Information Processing Systems 32, pp 8024&amp;#8211;8035, 2019.&lt;/p&gt; &lt;p&gt;7. Stenzel, O., Thor, R., and Hilchenbach, M.: Error identification in orbital laser altimeter data by machine learning, EGU General Assembly 2021, online, 19&amp;#8211;30 Apr 2021, EGU21-14749, https://doi.org/10.5194/egusphere-egu21-14749, 2021.&lt;/p&gt;

Estimating Mass Balance of White Glacier using ICESat-2

2021 ◽  
Author(s):  
Rachel Lackey
Keyword(s):  
Mass Balance ◽  
Laser Beams ◽  
Altimeter Data ◽  
Altimetry Data ◽  
Laser Altimeter ◽  
Ice Cloud ◽  
Geodetic Technique ◽  
Travel Restrictions ◽  
The Difference ◽  
Satellite Altimetry Data

White Glacier is located on Axel Heiberg Island in Nunavut, Canada, and has had its mass balance actively monitored since 1960. Due to COVID-19 travel restrictions it not possible for researchers to travel to White Glacier and perform the measurements required. This results in gaps in data required to determine the mass balance for 2018-2020. In this study we aim to collect and process laser altimeter data to be interpolated to calculate an estimate of the Mass Balance of White Glacier. This study will be completed using a geodetic technique that utilizes the Ice Cloud and Elevation-2 (ICESat-2) satellite altimetry data. ICESat-2 is carrying ATLAS which is an Advanced Topographic Laser Altimeter that is equipped with six laser beams divided into three pairs that measure lidar altimetry to derive surface height. The longitude, latitude, datetime, and land ice height values were extracted over the Expedition fjord region using MATLAB. The land ice tracks were brought into ArcGIS for analysis, three repeat tracks in the Expedition Fjord region were selected for analysis to determine the difference in elevation between the premelt seasons of 2019 and 2020 as well as one track comparing the premelt and melt seasons of 2019. These elevation differences will be interpolated as accumulation or ablation dependant on the location on the glacier and used to estimate mass balance.

scholarly journals POINTING BIAS CALIBRATION OF GAOFEN-7 LASER ALTIMETER BASED ON SINGLE LASER FOOTPRINT IMAGE

2020 ◽  
Vol V-2-2020 ◽  
pp. 113-119
Author(s):  
J. Xie ◽  
R. Liu ◽  
F. Mo ◽  
H. Tang ◽  
H. Jiao ◽  
...  
Keyword(s):  
Control Point ◽  
Field Work ◽  
Calibration Method ◽  
Laser Spot ◽  
Surface Model ◽  
Calibration Model ◽  
Digital Surface Model ◽  
Laser Altimeter ◽  
Geometric Calibration ◽  
Before And After

Abstract. The GaoFen-7 (GF-7) satellite is successfully launched on November 3, 2019, and its laser altimeter system is officially and firstly employed as the main payload for earth observations in China, which includes two sets of laser altimeters and laser footprint cameras. The Laser Footprint Image (LFI) is used to capture laser spots on the ground. In order to make up for the shortcomings of high cost field work for the traditional laser altimeter ground detector-based calibration method, this paper proposes a novel laser altimeter calibration method based on LFI. Firstly, the spaceborne laser calibration model and the Laser Footprint Camera (LFC) geolocation model are established. Secondly, the image coordinates of laser spot centroid are extracted from LFI, and the ground location of is obtained by ray intersecting with the reference Digital Surface Model (DSM). Finally, the centroid of laser spot is considered as Ground Control Point (GCP), and the pointing bias of GF-7 laser altimeter is calibrated by the Least Squares Estimation (LSE). The ALOS Global Digital Surface Model “ALOS World 3D-30m” (AW3D30) is used to evaluate the elevation accuracy of GF-7 laser altimeter before and after the calibration. The results indicate that elevation accuracy of the GF-7 laser altimeter is improved significantly after calibration. The proposed method can be effectively applied for high-frequency geometric calibration of GF-7 laser altimeter.

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 Combined Block Adjustment for Optical Satellite Stereo Imagery Assisted by Spaceborne SAR and Laser Altimetry Data

2021 ◽  
Vol 13 (16) ◽  
pp. 3062
Author(s):  
Guo Zhang ◽  
Boyang Jiang ◽  
Taoyang Wang ◽  
Yuanxin Ye ◽  
Xin Li
Keyword(s):  
Large Scale ◽  
Satellite Image ◽  
Field Measurements ◽  
High Elevation ◽  
Global Scale ◽  
Stereo Image ◽  
Image Process ◽  
Control Points ◽  
Altimetry Data ◽  
Laser Altimetry

To ensure the accuracy of large-scale optical stereo image bundle block adjustment, it is necessary to provide well-distributed ground control points (GCPs) with high accuracy. However, it is difficult to acquire control points through field measurements outside the country. Considering the high planimetric accuracy of spaceborne synthetic aperture radar (SAR) images and the high elevation accuracy of satellite-based laser altimetry data, this paper proposes an adjustment method that combines both as control sources, which can be independent from GCPs. Firstly, the SAR digital orthophoto map (DOM)-based planar control points (PCPs) acquisition is realized by multimodal matching, then the laser altimetry data are filtered to obtain laser altimetry points (LAPs), and finally the optical stereo images’ combined adjustment is conducted. The experimental results of Ziyuan-3 (ZY-3) images prove that this method can achieve an accuracy of 7 m in plane and 3 m in elevation after adjustment without relying on GCPs, which lays the technical foundation for a global-scale satellite image process.

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