An Attention Based Complex-valued Convolutional Autoencoder for GEO SA-Bi SAR Ship Target Refocusing
Bibliographic record
Abstract
Since ship targets are defocused due to heave motions caused by ocean waves, and contaminated by background clutter under rough sea conditions, high-resolution (HR) ship imaging is a great challenge in a Geosynchronous spaceborne/ airborne bistatic synthetic aperture radar (GEO SA-Bi SAR) system. Inspired by the recent success of artificial neural network (ANN) and deep learning (DL) in optical image processing, an improved complex-valued convolutional autoencoder (ICV-CAE) based on attention mechanism is proposed for GEO SA-Bi SAR ship target imaging. Different from the conventional SAR imaging methods that depend on parameters estimation and motion compensation, the proposed ICV-CAE can be trained to learn the mapping relation between the defocused inputs and the HR SAR images of ship targets. Hence, the well trained ICV-CAE can be regarded as a refocusing processor and applied to the complex- valued GEO SA-Bi SAR signals after range compression to achieve the HR ship target refocusing and sea clutter suppression simultaneously. The correctness and effectiveness of the proposed algorithm is validated by the experiment results in both ship target refocusing and sea clutter suppression.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".