Wetland Classification Using Feature Combination Based on Physical and Data-Driven Model
Bibliographic record
Abstract
Efficient classification is crucial to mastering wetland land cover types and facilitating conservation. Due to the advantages of Synthetic Aperture Radar (SAR) imaging, it enables continuous monitoring and penetration through vegetation canopies. In general, wetland types in SAR imagery mainly rely on backscattering, which hard to distinguish and differentiate land cover using single features. Effectively leveraging SAR features can improve classification accuracy in wetlands. However, there is rarely research discussing feature effects in classification. In this paper, physical and data-driven-based feature extraction methods are analyzed in wetland classification. Firstly, physical and data-driven models are combined for feature extraction. Furthermore, the performance of common classifiers is compared to evaluate the effectiveness of different feature types, such as Support Vector Machine (SVM), Random Forest(RF), and Extreme Gradient Boosting(XGB). The experimental results indicate that the combined feature extraction method of the physical and data-driven model performs the best in classifying the Yellow River Delta. It also improves the accuracy of all the classifiers in the comparative experiment. Notably, the RF classifier achieves the highest classification accuracy, with an overall accuracy(OA) of 94% and a Kappa coefficient of 0.93.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".