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Entropy-Based Doppler Centroid Estimation and Speckle Noise Reduction for Spaceborne SAR Imaging

2025· article· en· W4413096248 on OpenAlexaffabout
Shahrokh Hamidi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSpeckle patternSpeckle noiseSynthetic aperture radarComputer scienceArtificial intelligenceDoppler effectNoise reductionCentroidComputer visionDoppler imagingEntropy (arrow of time)Remote sensingGeologyPhysicsRadiologyMedicine

Abstract

fetched live from OpenAlex

In this paper, we present strip-map mode spaceborne Synthetic Aperture Radar (SAR) imaging with the focus on Doppler centroid frequency estimation. The non-zero Doppler centroid frequency is the result of nonzero squint angle which if it is not compensated it can de-focus the image. We present an efficient method based on the entropy of the reconstructed image to estimate the fractional part of the Doppler centroid frequency. Furthermore, we discuss the speckle noise, which degrades the quality of the reconstructed images considerably, and attempt to alleviate its effect efficiently. Following the implementation of the speckle noise reduction algorithm, a significant improvement in the quality of the reconstructed images is achieved. Finally, we utilize the experimental data gathered from the RADARSAT-1 satellite of Vancouver Canada to verify the accuracy and effectiveness of the proposed techniques.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.626
Threshold uncertainty score0.522

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 head0.005
GPT teacher head0.246
Teacher spread0.241 · 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
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

Citations2
Published2025
Admission routes2
Has abstractyes

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