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Record W2529189634 · doi:10.14358/pers.82.10.767

The Influence of Elliptical Gaussian Laser Beam on Inversion of Terrain Information for Satellite Laser Altimeter

2016· article· en· W2529189634 on OpenAlexfundno aff
Song Li

Bibliographic record

VenuePhotogrammetric Engineering & Remote Sensing · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaUniversity of LethbridgeNational Aeronautics and Space Administration
KeywordsAltimeterTerrainRemote sensingGeodesyGeologyInversion (geology)SatelliteLaserGeographyGaussian beamOpticsBeam (structure)CartographySeismologyPhysicsEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Abstract The transmitted laser mode of Geosciences Laser Altimeter System (GLAS) is a significant factor in determining the received pulse waveforms, which are used for inversing target information. The inversion algorithms in the scientific literature are based on the assumption that the transmitted laser is circular Gaussian. The practical laser pattern of GLAS is not circularly symmetric, but elliptical Gaussian. The received pulse shape will contain a bias, which would cause an error in the inversion information. In this paper, we describe new theoretical models about received pulse signal and inversion errors of range, surface slope and roughness. We present the results of waveforms shape and inversion errors for three representative terrains with different surface slope and roughness. The results show that the maximal inversion errors of range, surface slope, and roughness will reach 24.25 cm, 8.82° and 4.58 m, respectively, which cannot be negligible. Therefore, the inversion information should be reevaluated and amended depending on the type of terrain.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.207
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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