Evaluation of multiple SAR speckling filter techniques performance in irrigated rice areas
Bibliographic record
Abstract
Abstract. Monitoring irrigated rice crops is essential for efficient agricultural management, and Synthetic Aperture Radar (SAR) images are beneficial due to their capability to function in all weather conditions. However, speckle noise in SAR images complicates the classification of land cover. This study evaluates speckle filtering techniques to enhance the image quality of SAR for rice field monitoring. We extend previous work by comparing Bayesian filters in the spatial domain with advanced transform domain methods, including block matching 3D (BM3D) and Discrete Fourier Transform-extracted Edge (DFT Edge) techniques, across different rice growth stages. Twenty-two Sentinel-1B SAR images from the municipality of Turvo, Santa Catarina, Brazil were analyzed. Filters were assessed using metrics for speckle suppression and edge preservation. Our experiments reveal that BM3D with a sigma parameter of 10 (BM3Dsigma10 ) provided superior results, balancing effective speckle suppression and edge preservation. Specifically, BM3Dsigma10 was superior in preserving edge details compared to other techniques. However, as the σ value increased, a loss in resolution was observed, even for the DFT Edge method, which, while efficient in edge preservation, often resulted in reduced detail resolution. These findings highlight the importance of selecting appropriate filtering techniques for accurate agricultural monitoring using SAR data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".