SLA-Net: A Novel Sea–Land Aware Network for Accurate SAR Ship Detection Guided by Hierarchical Attention Mechanism
Bibliographic record
Abstract
In recent years, deep learning (DL)-based synthetic aperture radar (SAR) ship detection has made significant strides. However, many existing DL-based SAR ship detection methods treat sea regions and land regions equally, failing to be fully aware of the differences between the two regions during training and testing. This oversight may prevent the network’s attention from fully concentrating on valuable regions (i.e., sea regions and ship regions), thereby adversely affecting overall detection accuracy. To address these issues, we propose the Sea–Land Aware Net (SLA-Net), which introduces a novel SLA Hierarchical Attention mechanism to gradually focus the network’s attention on sea and ship regions across different stages. Specifically, SLA-Net instantiates the SLA Hierarchical Attention mechanism through three components: the SLA Sea-Attention Backbone, which incorporates sea attention in the feature extraction stage; the SLA Ship-Attention FPN, which implements ship attention in the feature fusion stage; and the SLA Ship-Attention Detection Heads, which enforce ship attention in the detection refinement stage. Moreover, to tackle the lack of sea–land priors in the community working on DL-based SAR ship detection, we introduce the sea–land segmentation dataset for SSDD (SL-SSDD). Built upon the well-established SAR ship detection dataset (SSDD), it serves as a synergistic dataset for ship detection when used in conjunction with SSDD. Quantitative experimental results on SSDD and generalization results on HRSID and LS-SSDD demonstrate that SLA-Net achieves superior SAR ship detection performance compared to other methods. Furthermore, SL-SSDD, which contains sea–land segmentation information, can provide a new perspective for the community working on DL-based SAR ship detection.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".