Crop Classification Using Multi-Temporal RADARSAT Constellation Mission Compact Polarimetry SAR Data
Bibliographic record
Abstract
The RADARSAT Constellation Mission (RCM) Compact Polarimetry (CP) data has become a key asset in crop mapping and monitoring for diverse agricultural landscapes. This study utilizes the unique capabilities of the RCM CP data for crop mapping. It performs a detailed comparison between single-date and multi-date classification to underscore the prowess of multi-temporal CP data in crop mapping. The novelty of our approach is in the thorough investigation of real CP data, a significant advancement from previous studies that mainly relied on simulated CP data. The CP data used in this study were acquired on July 1, July 30, and August 27, 2021, over southern Quebec, Canada, including soy, corn, hay, and cereal classes. Various features were extracted from the CP data, and the Random Forest classifier was utilized for crop mapping. The experimental results demonstrated the superiority of multi-temporal CP data for crop classification. The Overall Accuracy (OA) for single-date classifications on July 1, July 30, and August 27 were 61.10%, 75.00%, and 86.45%, respectively. In contrast, the multi-date analysis showed a marked increase in OA (91.20%). This substantial improvement underscores the significant benefit of incorporating multi-date CP data, which delivers a robust and precise framework for crop mapping.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".