Application of neural networks for wetland classification in RADARSAT SAR imagery
Bibliographic record
Abstract
The purpose of this study was to evaluate the ability of backpropagation neural networks to delineate forested and open wetlands and to distinguish between wetland categories using RADARSAT SAR data. To accomplish this objective, a multi-temporal dataset of RADARSAT images was used to evaluate the utility of the neural network approach for monitoring wetland vegetation communities and to detect seasonal changes in the Lac Saint-Jean region (Quebec, Canada). In order to accomplish this task, several parameters must be supplied, including the number of hidden nodes, learning, training, and ancillary data, such as textural information. To improve the neural classification performance, several techniques have been tested. In this way, a new methodology is developed for selection of training data sets and development of neural network structure. The advantages of neural networks for extracting information from radar backscattered energy are discussed with respect to classification accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".