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Record W4406856685 · doi:10.1109/lgrs.2025.3535220

Performance Simulation of SmallSat SAR Persistent Scatterer Interferometry With Deteriorated Images

2025· article· en· W4406856685 on OpenAlexaff
Jan Krecke, Oleksiy S. Kim, Michelangelo Villano, Gerhard Krieger, Mohammed Dabboor, John Cater, Andrew C. M. Austin

Bibliographic record

VenueIEEE Geoscience and Remote Sensing Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsEnvironment and Climate Change Canada
FundersMinistry of Business, Innovation and Employment
KeywordsInterferometrySynthetic aperture radarRadar imagingComputer scienceRemote sensingOpticsComputer visionGeologyPhysicsRadarTelecommunications

Abstract

fetched live from OpenAlex

The performance tradeoffs required for a small satellite synthetic aperture radar (SAR) system designed to measure surface deformations using persistent scatterer interferometry (PSI) are investigated. Existing X-band satellite data is systematically deteriorated to account for the increased range resolution and noise-equivalent sigma zero (NESZ). It is found that with an NESZ below 0 dB, the deformation signal of a selected region of interest (ROI) can be captured with mean absolute errors of 5 and 15 mm, for ground range resolutions of 5 and 20 m, respectively. This analysis is used to develop preliminary SAR system designs suitable for small satellites.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.385

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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