MétaCan
Menu
Back to cohort
Record W3109445726 · doi:10.1117/12.693734

Airborne multidimensional integrated remote sensing system

2006· article· en· W3109445726 on OpenAlexaboutno aff
Weiming Xu, Jianyu Wang, Rong Yang Shu, Zhiping He, Yanhua Ma

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicRemote Sensing and Land Use
Canadian institutionsnot available
Fundersnot available
KeywordsRemote sensingComputer scienceGeology

Abstract

fetched live from OpenAlex

In this paper, we present a kind of airborne multidimensional integrated remote sensing system that consists of an imaging spectrometer, a three-line scanner, a laser ranger, a position & orientation subsystem and a stabilizer PAV30. The imaging spectrometer is composed of two sets of identical push-broom high spectral imager with a field of view of 22°, which provides a field of view of 42°. The spectral range of the imaging spectrometer is from 450nm to 900nm, and its spectral resolution is 5nm. The three-line scanner is composed of two pieces of panchromatic CCD and a RGB CCD with 20 ° stereo angle and 10cm GSD(Ground Sample Distance) at 1000m flying height. The laser ranger can provide height data of three points every other four scanning lines of spectral imagers and those three points are calibrated to match the corresponding pixels of the spectral imagers. The post-processing attitude accuracy of POS/AV 510 used as the position & orientation subsystem, which is the aerial special exterior parameters measuring product of Canadian Applanix Corporation, is 0.005 ° combined with base station GPS data. The airborne multidimensional integrated remote sensing system was implemented successfully, performed the first flying experiment on April, 2005, and obtained satisfying data. 1.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.196
Teacher spread0.187 · 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 teacher head, 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

Citations0
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicRemote Sensing and Land UseFrench-language works237,207