Multi-temporal PolSAR Image Classification Using F-SAE-CNN
Why this work is in the frame
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Bibliographic record
Abstract
Crop classification using polarimetric SAR data is one of the most important applications in Polarimetric Synthetic Aperture Radar (PolSAR) imagery. Obviously, for crop classification, multi-temporal PolSAR data can provide more information than single-temporal PolSAR data, but the processing method of the matching image data is relatively backward. Aiming at the high-dimensional data composed of multi-temporal PolSAR, this paper proposes a method to integrate the stacked auto-encoder network and convolutional neural network, making full use of the dimension reduction advantages of the stacked auto-encoder network and the superior classification performance of the convolutional neural network. By constructing a fusion network, the multi-temporal PolSAR images can be processed once, the classification accuracy can be improved, and the processing steps can be simplified. The experimental results show that, compared with the traditional Stacked Auto-encoder and Convolutional Neural Network (SAE-CNN) classification method, the multitemporal PolSAR image classification method based on Fusion of Stacked Auto-encoder and Convolutional Neural Network (F-SAE-CNN) proposed in this paper has the highest classification accuracy, which effectively combines the advantages of the self-encoding network and the CNN network, and provides a new idea for PolSAR image classification work.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it