Wet-GC: A Novel Multimodel Graph Convolutional Approach for Wetland Classification Using Sentinel-1 and 2 Imagery With Limited Training Samples
Bibliographic record
Abstract
Wetland is one of the most productive resources on Earth, and it provides vital habitats for several unique species of flora and fauna. Over the last decade, mapping and monitoring wetlands by utilizing deep learning (DL) models and Remote Sensing (RS) data gained popularity due to the importance of wetland preservation. In general, DL-based methods have shown astonishing achievement in wetland classification, but some practical issues, such as limited training samples, still need to be addressed. Moreover, the performance of most of the DL approaches is decreased when moderate-resolution images with few features are used as input data. One solution to breaking the performance bottleneck of a single model is to fuse two or more of them. To this end, we strive to investigate and develop a multi-model DL algorithm for wetland classification based on the combination of a Graph Convolutional Network (GCN) and a shallow Convolutional Neural Network (CNN), which is called the Wet-GC algorithm hereinafter. In doing this, moderate-resolution Sentinel-1 (S1) Synthetic Aperture Radar (SAR) and Sentinel-2 (S2) multispectral optical imagery are fed into the GCN and CNN models, respectively. As we know from the literature, the synergistic use of S1 SAR and S2 optical imagery can be used to extract different types of wetland features and increase the class discrimination possibility. Hence, wetland mapping by jointly using GCN and CNN has the ability to boost the wetland classification task. Findings indicate that the efficiency of Wet-GC with an Overall Accuracy (OA) of 88.68% outperforms the results obtained from Random Forest (OA = 84.88%), Support Vector Machine (OA = 82.86%), Extreme Gradient Boosting (OA = 86.55%), and ResNet50 (OA = 86.93). The outcomes reveal that the Wet-GC architecture proposed in this study has an excellent capability to be applied over large areas with minimal need for training samples and can perform acceptably in supporting regional wetland mapping.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".