Coastal Wetland Mapping Using CNN_RF based on Multi-Temporal and Multi-Source Sensors
Bibliographic record
Abstract
In order to acquire classification results from limited ground truth data to other years and address the challenges in extracting high-quality features with limited samples, a novel coastal wetland mapping using CNN_RF based on multi-temporal and multi-source sensors is proposed. Firstly, change detection using the Gaussian Mixture Model (GMM) is applied to Sentinel-1 and Sentinel-2 data, leveraging spectral and backscatter features to accurately identify unchanged areas. Subsequently, a Convolutional Neural Network (CNN) is employed for feature extraction, enabling the extraction of deeper, abstract features from the images. Finally, Random Forest (RF) is used for mapping, capitalizing on its ability to process high-dimensional data effectively. The experimental results demonstrate that the proposed method achieved excellent class separability, with an average overall accuracy (OA) of 88.2% and an average Kappa coefficient of 0.853. In addition, the CNN_RF classification conducted on the Yellow River Delta from 2019 to 2024 reveals that the extensive eradication of Spartina alterniflora is achieved as a result of government intervention.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".