On the Observation of Water Bodies by Means of Compact Polarimetric Synthetic Aperture Radar Imagery
Bibliographic record
Abstract
Water bodies, including lakes and rivers, represent extraor-dinary sources for ecology, biodiversity, irrigation and water supply. As a matter of fact, they impact on several aspects of human, animal and vegetation living. Hence, continuous and reliable monitoring of the water bodies extent is of paramount importance and must be performed by high-quality techno-logical tools. Spaceborne detection of changes associated to natural and human-induced water dynamics is of great im-portance for ecosystem preservation, disaster warnings and water conservation projects. For this purpose, it is well estab-lished that satellite remote sensing tools represent an invalu-able source of information. Under this framework, this study focuses on the analysis of compact-polarimetric (CP) C-band Synthetic Aperture Radar (SAR) imaging modes to extract lake shoreline and, therefore, monitor the water-covered area. A reference shoreline extraction scheme is adopted for this purpose, which is feed by po-larimetric features associated to the correlation of backseat-tering channels, namely δ. The lake Athabasca in Alberta, Canada, is selected as a meaningful test site. The experimen-tal results show that the CP C-band SAR measurements can be effectively used to get lake extent information.
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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".