Study and assessment of selected primitive features behaviour for SAR image description
Bibliographic record
Abstract
The main purpose of this study is to define for Synthetic Aperture Radar (SAR) data the primitive feature parameters, the incidence angle, and the orbit direction which can be used further for indexing and querying in the EO systems. The evaluation is done on the high resolution SAR data and the interpretation is realized automatically. In this paper, we propose to study and asses the behavior of the primitive feature extracted methods for images of the same scene with two look angles covering the min-max range of the sensor and with ascending / descending orbit looking. The tests are done on TerraSAR-X products Stripmap and high resolution Spotlight, specially and radiometrically enhanced covering the area of Berlin (Germany) and Ottawa (Canada). To identify the optimal primitive features, incident angle, and orbit direction the Support Vector Machine and as a measure of the classification accuracy the precision/recall were considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".