Integration of multispectral and C-band SAR data for crop classification
Bibliographic record
Abstract
The paper debates the impact of sensor configuration diversity on the crop classification performance. More specifically, the analysis accounts for multi-temporal and polarimetric C-Band SAR information used individually and in synergy with Multispectral imagery. The dataset used for the investigation comprises several multi-angle Radarsat-2 (RS2) fullpol acquisitions and RapidEye (RE) images both at fine resolution collected over the Indian Head (Canada) agricultural site area and spanning the summer crop growth cycle from May to September. A quasi-Maximum Likelihood (ML) classification approach applied at per-field level has been adopted to integrate the different data sources. The analysis provided evidence on the overall accuracy enhancement with respect to the individual sensor performances, with 4%-8% increase over a single RE image, a 40%-10% increase over a single 1-pol/full-pol image and 15%-0% increase over multitemporal 1-pol/full-pol RS2 series respectively. A more detailed crop analysis revealed that in particular canola and the cereals benefit from the integration, whereas lentil and flax can experience similar or worse performance when compared to the RE-based classification. Comments and suggestions for further development are presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".