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Record W2973474242 · doi:10.1109/radar.2019.8835641

Ship Detection, Discrimination, and Motion Estimation via Spaceborne Polarimetric SAR-GMTI

2019· article· en· W2973474242 on OpenAlexaff
Shen Chiu, Christoph H. Gierull, Mamoon Rashid

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsDepartment of National Defence
Fundersnot available
KeywordsSynthetic aperture radarRemote sensingEarly-warning radarPolarimetryComputer scienceMoving target indicationSpace-based radarInverse synthetic aperture radarRadar imagingRadarContinuous-wave radarGeologyPhysicsTelecommunicationsOpticsScattering

Abstract

fetched live from OpenAlex

A new approach to ship detection, discrimination, and motion estimation is proposed and demonstrated using a combined polarimetry and GMTI technique from a spaceborne platform. Using a standard dual-pol SAR, the new method exploits a bright “shadow” in cross-pol imagery of a moving ship and its azimuth-shifted image to discriminate it from other stationary or quasi-stationary objects (e.g., icebergs) on the ocean surface and to estimate its motion parameters, such as radial speed and heading. The motion estimation results are compared to those obtained using the newly implemented Dual-Channel Polarimeric GMTI modes on RADARSAT-2. Unlike the ship wake approach, the proposed technique is applicable to and successfully demonstrated for both C-band and X-band radars (RADARSAT-2 and TerraSAR-X) and it is not dependent on the angle of incidence to achieve a good signal-to-clutter ratio and ship detectability. Physical mechanisms giving rise to ship “shadows” are also discussed.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.472

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 studies0.0000.000
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 head0.006
GPT teacher head0.206
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations6
Published2019
Admission routes1
Has abstractyes

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