PolSARConvMixer: A Channel and Spatial Mixing Convolutional Algorithm for PolSAR Data Classification
Bibliographic record
Abstract
Given the exceptional effectiveness of deep Convolutional Neural Networks (CNNs) in computer vision, there has been a recent surge of interest in employing CNNs for various applications in image classification. Additionally, scientists are exploring the potential of vision transformers for Earth observation applications, owing to their recent tremendous success. However, a major challenge with vision transformers is their increased demand for training data compared to CNN classifiers. Furthermore, vision transformers exhibit quadratic complexity and necessitate substantial hardware resources. In the context of PolSAR image classification, we propose the PolSARConvMixer—a fundamental framework that segregates the mixing of spatial and channel dimensions, maintains uniform size and resolution across the network and directly processes PolSAR image patches as input. Our experiments on two PolSAR data benchmarks, namely Flevoland and San Francisco, demonstrate the significant superiority of the developed PolSARConvMixer over several other algorithms, including AlexNet, ResNet, FNet, a 2D CNN, and PolSARFormer.
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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 | 0.001 | 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) | 0.004 | 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".