ConvAttentionNet: a high-performance model for efficient and accurate PolSAR data classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents ConvAttentionNet, a lightweight and high performing deep learning model developed for accurate and efficient classification of Polarimetric Synthetic Aperture Radar (PolSAR) imagery. The proposed architecture combines multiscale convolutional mixer blocks with a directional convolution based attention mechanism to effectively capture spatial features and suppress background noise. Designed to address the challenges of limited labeled data and computational constraints, ConvAttentionNet achieves superior performance while maintaining a compact model size. Experimental results on three benchmark datasets (Flevoland, San Francisco, and Oberpfaffenhofen) demonstrate that ConvAttentionNet consistently outperforms state of the art CNN based, transformer based, and wavelet based models. It achieves an overall accuracy (OA) of 97.24% and a Kappa coefficient of 96.98 on the Flevoland dataset using only 1% of the training data. These results confirm the model’s robustness, label efficiency, and generalization capabilities, making it a practical solution for operational remote sensing scenarios with limited computational resources. The source code for this work will be publicly available at: https://github.com/aj1365/ConvAttentionNet .
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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 | 0.000 |
| 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 it