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Record W2049601223 · doi:10.1117/12.830548

RapidEye product quality assessment

2009· article· en· W2049601223 on OpenAlexaff
Keith Beckett, Chris Rampersad, Rony Putih, B.C. Robertson, Joe Steyn, George Tyc

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldEngineering
TopicSatellite Image Processing and Photogrammetry
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsCalibrationRemote sensingRadiometric calibrationSatelliteGround segmentComputer scienceProduct (mathematics)Quality (philosophy)ConstellationRadiometryQuality assessmentEnvironmental scienceGeographyEngineeringEvaluation methodsReliability engineeringMathematicsAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

Geometric and radiometric product quality are critical to enable the use of remotely sensed imagery [1,2]. Over a period of nine months following the launch, the constellation of five RapidEye satellites underwent an iterative process of commissioning, calibration and product quality assessment. This paper describes the post-launch calibration techniques used to characterize the payloads, summarizes the calibration results, and documents the product quality achieved. It illustrates how ground-based post-launch calibration techniques were successfully used to mount a geometric and radiometric calibration campaign consistent with a small-sat satellite mission, to produce high quality imagery products.

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.006

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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations8
Published2009
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSatellite Image Processing and PhotogrammetryFrench-language works237,207