The effect of some internal neural network parameters on SAR texture classification performance
Bibliographic record
Abstract
Artificial neural networks have been successfully applied to image processing, and have shown a great potential in the classification of a wide range of remote sensing data. The major advantages of neural network algorithm over traditional classifiers are its nonparametric nature and its easy adaptation to different types of data format from multiple sources. However, a successful application of neural networks in remote sensing data classification requires a good comprehension of the effect of some internal parameters related to the neural network structure and training process. In this work we report the application of backpropagation neural network in classifying natural wetlands vegetation using SAR data. The effect of some parameters related to the architecture and the training process on classification performance was investigated and new techniques for ameliorating this performance are discussed. The results showed that the variations of the number of hidden layers and the number of nodes by layer have not a substantial effect on classification accuracy but affect only the training time. However, other parameters related to the neural algorithm computation (such as the threshold value) affect significantly the overall classification. It is concluded that, although the neural network method have a great potential in remote sensing data classification, a rigorous choice of the threshold value still necessary to optimize the ratio of the incorrectly and the correctly classified pixels
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".