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Record W2440513083 · doi:10.1109/radar.2016.7485171

Coherent ground mapping of polar format images with applications to high-resolution wide-area SAR imaging

2016· article· en· W2440513083 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPolarSynthetic aperture radarComputer scienceRemote sensingImage resolutionArtificial intelligenceHigh resolutionRadar imagingComputer visionResolution (logic)Image (mathematics)GeologyRadarPhysicsTelecommunications

Abstract

fetched live from OpenAlex

In this article, we consider some approaches to using the polar format algorithm for high-resolution wide-area synthetic aperture radar (SAR) imaging. We will broadly discuss two general approaches to extending the polar format algorithm to produce focused high-resolution imagery over wide areas. First, we will describe a fast backprojection-like algorithm based on coherently mapping polar formatted subapertures to the ground and coherently combining the ground-mapped images. Second, we will discuss an alternative approach to generating high-resolution wide-area imagery, which starts with an initial polar format image and then subsequently refines the image in subpatches in a coherently consistent manner across the image. Central to both methods is a general framework for coherently mapping polar format images to the ground.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.737
Threshold uncertainty score0.449

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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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