An evaluation of Radarsat-2 individual and combined image dates for land use/cover mapping
Bibliographic record
Abstract
Various land use/cover types exhibit seasonal characteristics which can be captured in remotely sensed imagery. This study examined how different seasons of Radarsat-2 data influence land use/cover classification accuracies for two study sites. Two dates of Radarsat-2 C-band quad-polarised images were obtained for Washington, DC, USA and Wad Madani, Sudan. Spectral signatures were extracted and used with a maximum likelihood decision rule for classification and thematic accuracies were then determined. Both despeckled radar and derived texture measures were examined. Thematic accuracies for the two despeckled image dates were similar with a difference of 3% for Washington and 6% for Sudan. Merging the despeckled images for both seasons increased overall accuracy by 2% for Washington and 9% for Sudan. Further combining the original radar for both seasons with derived texture measures increased overall accuracies by 9% for Washington and 16% for Sudan for final overall accuracy values of 73 and 82%.
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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 | 0.004 | 0.006 |
| 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 | 0.001 | 0.001 |
| 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".