Effective Segmentation of X-Band Marine Radar Images Using SegFormer
Bibliographic record
Abstract
X-band marine radar imaging has been widely used for ocean surface monitoring and parameter estimation. However, the quality of radar images can be significantly affected by various meteorological conditions, leading to inaccurate estimation of ocean surface parameters. In this paper, a novel approach is proposed for segmenting X-band marine radar images into four distinct regions using the SegFormer model, an advanced semantic segmentation architecture that employs a hierarchical transformer encoder to capture global context and a lightweight all-multilayer perception (MLP) decoder to enable high-resolution segmentation. The four regions include areas with clear wave signatures, rain-contaminated areas, low backscatter areas, and wind-dominated rain areas. The experimental data collected by a shipborne Decca radar system is used to validate the effectiveness of the proposed method, and a SegNet-based segmentation approach is utilized for comparison. Experimental results demonstrate that the SegFormer-based method outperforms the SegNet-based method, with an improvement of 0.5% in mean segmentation accuracy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".