An Evidence Modified Gaussian Process Classifier (EM-GPC) for Crop Classification Using Dual-Polarimetric C- and L- Band SAR Data
Bibliographic record
Abstract
Accurate crop classification with synthetic aperture radar (SAR) data is a significant area of research and translating into practice from local to regional scale crop inventory mapping. With the growing accessibility to abundant data sources from both current and upcoming dual-polarimetric SAR missions, the capability to generate precise crop maps is set to enhance substantially. The geometric and dielectric properties of targets highly influence radar backscatter. Especially for agricultural crops, which exhibit dynamic changes in target properties and physiological structure throughout their phenology, discriminating between crops using SAR data remains a significant challenge. This study introduces a novel Gaussian process classifier model called the evidence modified Gaussian process classifier (EM-GPC) that employs a regression approach for multiclass crop classification with a modified evidence. We utilize dual-polarimetric SAR data acquired over two study sites to perform crop classification utilizing EM-GPC. At first, we perform a phenology-based single-date crop classification using ESAR C- and L-band SAR data acquired over the DEMMIN site in Germany. The performance evaluation of the EM-GPC model revealed robust accuracy during various crop phenological stages, showcasing its adaptability to temporal variations. Further, we perform a multidate crop classification using C-band RADARSAT-2 and L-band simulated NISAR product from UAVSAR data acquired over Manitoba, Canada. In this study, EM-GPC successfully classifies major crop types (wheat, canola, soybeans, corn, barley, oats, and rye). The efficacy of the proposed model establishes its capability for crop classification utilizing dual-polarimetric SAR data in operational settings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".