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Record W2469148510 · doi:10.14288/1.0065468

ScanSAR radiometric calibration based on roll angle Estimatiru

2009· article· en· W2469148510 on OpenAlexaboutno aff
D.C. Bast

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCalibrationRadiometric datingRemote sensingRadiometric calibrationRadiometryGeologyGeodesyMathematicsStatistics

Abstract

fetched live from OpenAlex

Wide-area SAR imagery obtained by the RADARSAT-1 ScanSAR mode can suffer from various radiometric artifacts. Some of these artifacts arise from the incorrect application of Range Dependent Gain Corrections due to insufficient knowledge of satellite roll angle. Specifically, roll angle estimation errors as small as 0.1 degrees can cause noticeable gain errors of 1 dB or more. Beam-stitching techniques exist that can reduce these errors in the beam overlap region; however, accurate roll information is required for optimum radiometric calibration across the entire range swath. Current roll angle estimation algorithms do not provide consistent results even on routine scenes. These algorithms are susceptible to uncertainties in the range beam patterns, overall scene a°, and other system variables. Currently, the Canadian Data Processing Facility does not implement an automated roll angle estimator. Compensation for range gain errors is performed in the post-processing stage. This thesis proposes a new data acquisition method, in which signal data is obtained during the beam switchover by transmitting pulses through one beam and receiving them with another beam. This "hybrid data" is then used in a new (hybrid peak detection) and modified (three beam) algorithm to provide a more accurate and robust roll estimate. Algorithms using this hybrid data are more tolerant to lower mean scene σ°, gain uncertainty, and other variables than algorithms using normal data. As this data is not currently acquired, the algorithm is tested using simulated data. The logistics of acquiring hybrid data are also explored. The implementation of hybrid data acquisition on RADARSAT-1 would not require any significant software changes. The effects of various roll angle estimation errors on different beam combinations are simulated. The new data and algorithms offer significant potential for improving roll estimates. Results suggest that the three beam algorithm can generally tolerate 3-4 dB lower σ° and 0.2 to 0.4 dB more uncertainty in the beam pattern gain than a current algorithm, while meeting required radiometric accuracy. The hybrid peak detection algorithm did not meet stringent roll requirements, but was shown to produce consistent coarse roll estimates while remaining independent of the mean scene σ and beam gain uncertainty. Other current algorithms can also be modified to use the hybrid data for potentially greater accuracy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.164
Teacher spread0.159 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2009
Admission routes1
Has abstractyes

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