Classification of multipolarisation radar images in agricultural areas
Bibliographic record
Abstract
The objective of this research is to study the contribution of multipolarisation airborne radar data to crop discrimination. An unsupervised classification algorithm and a supervised method based on maximum likelihood were used and compared for agricultural applications. The experimental area, in Southern Ontario, Canada, was chosen because it has been the site of extensive inventory in relation to the application of VIR and radar data for agricultural uses. C-HH, C-VV and C-HV data for 10 July 1990 were used for the study. At this time, crop development allows optimal separability between crops. Results show that multipolarized radar data offer an adequate tool for crop identification. Correct classification rates of 83% and 79% were obtained for supervised and unsupervised methods respectively. Comparison of the two methods reveals that the performance of the unsupervised classification is similar to that of the supervised classification. This is a promising result if the authors take into consideration the fact that the unsupervised classification eliminates tedious effort for data collection and that it makes mole efficient use of computer time in the training stage.>
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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".