Dual-Polarimetric SAR Imaging Modes to Monitor Lake Extent
Bibliographic record
Abstract
Water ecosystems as oceans, lakes and rivers contribute significantly to global biodiversity, influence the ecological balance and play a remarkable role in different aspects of human society and economy. Therefore, the detection of changes associated to natural and human-induced water dynamics is of great importance for ecosystem preservation, disaster warnings and water conservation projects. To this aims, it is well established that satellite remote sensing tools represent an invaluable source of information.Under this framework, this study focuses on the analysis of dual-polarimetric C-band synthetic aperture radar imaging modes, including compact and linear ones, to extract lake waterline and, therefore, monitor the water-covered area. A reference waterline extraction scheme is adopted for this purpose, which is feed by polarimetric features associated to the two backscattering channels, namely δ and ρ for the compact polarimetric Radarsat Constellation Mission and the linear polarization Sentinel-1 mission. The Athabasca lake in Alberta, Canada, is selected as a meaningful test site. The experimental results show that the dual-polarimetric C-band synthetic aperture radar data can be effectively used to get lake extent information, with subtle differences that apply between RCM compact-polarimetric and Sentinel-1 linear polarization imaging modes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".