SSL-MBC: Self-Supervised Learning With Multibranch Consistency for Few-Shot PolSAR Image Classification
Bibliographic record
Abstract
Deep learning methods have recently made substantial advances in polarimetric synthetic aperture radar (PolSAR) image classification. However, supervised training relying on massive labeled samples is one of its major limitations, especially for PolSAR images that are hard to manually annotate. Self-supervised learning (SSL) is an effective solution for insufficient labeled samples by mining supervised information from the data itself. Nevertheless, fully utilizing SSL in PolSAR classification tasks is still a great challenge due to the data complexity. Based on the abovementioned issues, we propose an SSL model with multibranch consistency (SSL-MBC) for few-shot PolSAR image classification. Specifically, the data augmentation technique used in the pretext task involves a combination of various spatial transformations and channel transformations achieved through scattering feature extraction. In addition, the distinct scattering features of PolSAR data are considered as its unique multimodal representations. It is observed that the different modal representations of the same instance exhibit similarity in the encoding space, with the hidden features of more modals being more prominent. Therefore, a multibranch contrastive SSL framework, without negative samples, is employed to efficiently achieve representation learning. The resulting abstract features are then fine-tuned to ensure generalization in downstream tasks, thereby enabling few-shot classification. Experimental results yielded from selected PolSAR datasets convincingly indicate that our method exhibits superior performance compared to other existing methodologies. The exhaustive ablation study shows that the model performance degrades when either the data augmentation or any branch is masked, and the classification result does not rely on the label amount.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".