Geometric and radiometric correction of ESA SAR products
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.
Bibliographic record
Abstract
Accurate geolocation of SAR imagery enables not only precise overlays with other data sources in a common map geometry, but also normalisation for the systematic influence of terrain on image radiometry. We begin by describing our verifications of the geometric behaviour of ENVISAT ASAR products, including all image mode (IM), alternating polarisation (AP), and wide swath (WS) types: IMS, IMP , IMM, IMG, APS, APP , APM, APG, WSM, and WSS. Radar transponders in Canada and Europe are used as easily identifiable targets in radar images to test the accuracy of the nominal timing and state vector annotations accompanying each product. Accuracies achievable using DORIS precise state vectors are also evaluated. In addition to ENVISAT's ASAR, geolocation accuracies achievable using ERS-1/2 and ALOS PALSAR data are demonstrated. Given accurate knowledge of the acquisition geometry of a SAR image from one of the above sensors together with a digital elevation model (DEM) of the area imaged, the process of terrain geocoding is used to transform a diverse set of images into a common reference map geometry. The prerequisite DEM combined with accurate knowledge of the acquisition geometry also enables a radiometric correction, whereby variations in terrain specific to each scene are normalised to a common standard. Thematic interpretation benefits from such pre-processing: we demonstrate improved thematic discriminations using product overlays in a common map geometry where radiometric terrain correction (RTC) has been applied in comparison to typical GTC results.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it