Accurate crop-type classification using multi-temporal optical and multi-polarization SAR data in an object-based image analysis framework
Bibliographic record
Abstract
Accurate crop-type classification is a challenging task due, primarily, to the high within-class spectral variations of individual crops during the growing season (phenological development) and, second, to the high between-class spectral similarity of crop types. Utilizing within-season multi-temporal optical and multi-polarization synthetic aperture radar (SAR) data, this study introduces a combined object- and pixel-based image classification methodology for accurate crop-type classification. Particularly, the study investigates the improvement of crop-type classification by using the least number of multi-temporal RapidEye (RE) images and multi-polarization Radarsat-2 (RS-2) data utilized in an object- and pixel-based image analysis framework. The method was tested on a study area in Manitoba, Canada, using three different classifiers including the standard Maximum Likelihood (ML), Decision Tree (DT), and Random Forest (RF) classifiers. Using only two RE images of July and August, the proposed method results in overall accuracies (OAs) of about 95%, 78%, and 93% for the ML, DT, and RF classifiers, respectively. Moreover, the use of only two quad-pol images of RS-2 of June and September resulted in OAs of 92%, 75%, and 90% for the ML, DT, and RF classifiers, respectively. The best classification results were achieved by the synergistic use of two RE and two RS-2 images. In this case, the overall classification accuracies were 97% for both ML and RF classifiers. In addition, the average producer’s accuracies of 95% and 96% were achieved by the ML and RF classifiers, respectively, whereas the average user accuracy was 94% for both classifiers. The results indicated promising potentials for rapid and cost-effective local-scale crop-type classification using a limited number of high-resolution optical and multi-polarization SAR images. Very accurate classification results can be considered as a replacement for sampling the agricultural fields at the local scale. The result of this very accurate classification at discrete locations (approximately 25 × 25 km frames) can be applied in a separate procedure to increase the accuracy of crop area estimation at the regional to provincial scale by linking these local very accurate spatially discrete results to national wall-to-wall continuous crop classification maps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".