Comparison of Speckle Noise Filters on Crop Classification Based on Sentinel-1 Sar Time-Series
Bibliographic record
Abstract
Knowing the spatial distribution of crops is key for the estimation of water consumption, crop yield, food policy, among others. In this study, dual-polarization (VV/VH) intensity time-series C-band Sentinel-1 sensor are used to perform crop classification with two models: Spectral Similarity Value SSV and Random Forest RF. Their performance was evaluated under three SAR speckle filter scenarios, for each polarization. Scenario 1 Sigma Lee, Scenario 2 IDAN, and Scenario 3 Non-Local Means. Crop classification was performed with 24 images of 2018. Ground-truth data for training/validation was obtained from the United States Department of Agriculture (USDA). RF shows better performance than SSV for all scenarios. Non-Local Means filter delivers the best results. VH polarization performs better that VV in all scenarios. The best accuracy for SSV model is 0.73, and for RF model is 0.95 both using VH polarization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".