Effects of incidence angles on mapping accuracy of surficial materials in the Umiujalik Lake area, Nunavut, using RADARSAT-2 polarimetric SAR images. Part 2. Polarimetric analysis
Bibliographic record
Abstract
Our study assesses the effect of incidence angle on classifications obtained using various polarimetric classifiers applied to polarimetric RADARSAT-2 synthetic aperature radar (SAR) images for mapping surficial materials in Arctic Canada. The RADARSAT-2 polarimetric SAR images were acquired over the Umiujalik Lake test area of Nunavut in three west-looking descending beam modes (FQ1, FQ12, and FQ20) with increasing respective incidence angles. Polarimetric analyses included computation of polarimetric signatures, Wishart supervised classification, as well as Wishart–H/, Wishart–H//A, and Freeman–Wishart unsupervised classifications. Polarimetric signatures helped to understand class separability as a function of the scattering mechanisms of the surficial materials considered in this study. The medium incidence angle (FQ12) image produced the best overall classification accuracy (48.7%) for the Wishart supervised classification. In general, the Freeman–Wishart unsupervised classification produced better areal distribution of surficial materials with the FQ12 and FQ20 images than with the steep angle FQ1 image. More sophisticated classification algorithms are required to combine the multibeam RADARSAT-2 polarimetric SAR images with other geospatial data such as optical images and digital elevation model data. The influences of variable environmental conditions (moisture and temperature) on mapping accuracy of surficial materials also require further research.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".