COMPARISON OF C-BAND AND X-BAND POLARIMETRIC SAR DATA FOR RIVER ICE CLASSIFICATION ON THE PEACE RIVER
Bibliographic record
Abstract
Abstract. In this study, synthetic aperture radar (SAR) data from TerraSAR-X were compared with RADARSAT-2 data to evaluate their effectiveness for river ice monitoring on the Peace River. For several years RADARSAT-2 data have been successfully used for river ice observation. However, it is important to take into account data from other satellites as they may provide solutions when it is not possible to obtain images from the preferred system (e.g., in the case of acquisition priority conflicts). In this study we compared three TerraSAR-X (X-band) and three RADARSAT-2 (C-band) datasets acquired in December 2013 on a section of the Peace River, Canada. For selected classes (open water, skim ice, juxtaposed skim ice, agglomerated skim ice, frazil run and consolidated ice) we compared backscattering values in HH and VV polarisation and performed Wishart supervised classification. Covariance matrices that were previously filtered using a refined Lee filter were used as input data for classification. For all data sets the overall accuracy was higher than 80%. Similar errors associated with classification output were observed for data from both satellite systems.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".