Polarimetric SAR for geomorphic mapping in the intertidal zone, Minas Basin, Bay of Fundy, Nova Scotia
Bibliographic record
Abstract
The purpose of this study was to assess the potential of airborne polarimetric Synthetic Aperture Radar (SAR) data for geomorphic mapping of intertidal terrain. The study is part of ongoing applications development research at the Canada Centre for Remote Sensing (CCRS) in preparation for the launch of RADARSAT-2 in 2003. Calibrated polarimetric Convair-580 SAR data were acquired during low tide conditions over the southern Bight of Minas Basin, Bay of Fundy, Nova Scotia, in November 1999. It is shown that there is a significant improvement in geomorphic target identification and discrimination when fully polarimetric SAR data are used. Qualitative analysis of the SAR power images reveals the relative importance of surface and vegetation scatter in the intertidal terrain. Strong backscatter contrasts in the linear polarizations enabled delineation of the boundaries between various tidal sub-environments. The cross-polarized channel return (s°HV) was the optimal polarization for delineating the mean high water line. The HH-polarized channel return (s°HH) was the optimal polarization for delineating the mean low water line and enabled differentiation of intertidal sediment classes. Comparison of co-polarized polarimetric response plots from different tidal sub-environments with previous studies reveals the importance of surface roughness as the dominant target scattering mechanism in the intertidal zone. An unsupervised classification of target scattering behaviour shows good agreement with the known distribution of intertidal vegetation and sediment characteristics.
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 | 0.000 | 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".