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Record W4312228669 · doi:10.1109/lgrs.2022.3229624

Signatures of Small Boats With TerraSAR-X Staring Spotlight Data

2022· article· en· W4312228669 on OpenAlexaff
Igor Zakharov, Michael D. Henschel, Desmond Power, Pamela E. Burke, Thomas Puestow, Sherry Warren

Bibliographic record

VenueIEEE Geoscience and Remote Sensing Letters · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsNational Research Council CanadaCentre For Cold Ocean Resources Engineering
Fundersnot available
KeywordsStaringSynthetic aperture radarRemote sensingSatelliteComputer scienceRadar imagingImage resolutionSide looking airborne radarHigh resolutionGeologyRadarComputer visionContinuous-wave radarTelecommunicationsEngineeringOpticsPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

Spaceborne synthetic aperture radar (SAR) is an important technology for ship detection applications. It can provide timely information on small boat locations, which is important for security and safety applications. This letter describes the advantages of using very-high-resolution TerraSAR-X data acquired in staring spotlight mode for detecting small boats. Coincident to the SAR image acquisitions, electro-optical (EO) satellite imagery was used, together with field photographs of boats. The results demonstrate the ability to distinguish SAR signatures of small wooden and fiberglass vessels with the size of up to 4 m in length.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.034
GPT teacher head0.210
Teacher spread0.176 · 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 designNot applicable
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

Citations6
Published2022
Admission routes1
Has abstractyes

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