Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".