Crop Classification Using Fully Polarimetric SAR Imagery
Bibliographic record
Abstract
An important prerequisite for improving the classification accuracy is to fully extract the characteristics that reflect physical properties of the objects. The objective of this study is to investigate the capability of quad polarized Synthetic Aperture Radar (SAR) images for crop classification in Ontario, Canada. Multi-temporal RADARSAT-2 fine beam quad-polarized SAR data were acquired. A support vector machine (SVM) classifier was selected for the classification using combinations of the polarization characteristics and texture features. The polarimetric features, including odd scattering, double scattering and volume scattering, were extracted from classic Pauli decomposition. Eight texture features were extracted from grey level co-occurrence matrix (GLCM). Principal Component Analysis (PCA) method was applied to reduce the redundancy of texture features. The results indicated that multi-temporal SAR data achieved satisfactory classification accuracy. Texture features of SAR data were useful for improving classification accuracy. SAR data have considerable potential for agricultural monitoring.
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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 | 0.001 | 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".