Assessment of multi-temporal RADARSAT-2 polarimetric SAR data for crop classification in an urban/rural fringe area
Bibliographic record
Abstract
This paper investigated the potential of multi-temporal polarimetric RADARSAT-2 data for crop classification in an urban/rural fringe area. Using five scenes of RADARSAT-2 fine beam Quadpol data acquired during the 2012 growing season, five main crop types (wheat, soybeans, corn, field peas, and forage) in Southwestern Ontario, Canada have been identified. The potential of the RADARSAT-2 data for crop classification was assessed on four aspects: (1) the selection of classifier, (2) the effectiveness of polarimetric parameters, (3) the combination of multi-temporal data, and (4) post-classification processing methods. Pauli decomposition parameters proved to be effective in crop classification using Gaussian based Maximum Likelihood Classifier. With five dates of the images, the five crop types and other four non-crop types were classified at an overall accuracy of 91%. Satisfactory results with an overall accuracy of 87.8% were achieved by using only three dates of data given that the images covering the critical crop growth stages were included. Results demonstrate that polarimetric RADARSAT-2 data are suitable for accurate crop mapping in urban/rural fringe areas.
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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 | 0.000 |
| 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".