S2G-GCN: A Plot Classification Network Integrating Spectrum-to-Graph Modeling and Graph Convolutional Network for Compact HFSWR
Bibliographic record
Abstract
Plot classification refers to the identification of true target plots among initial detections, and it is crucial for target tracking with compact high-frequency surface wave radar (HFSWR) systems. However, due to the limited spatial resolution and low signal to interference plus noise ratio (SINR) inherent in compact HFSWR systems, traditional classification methods often fail to distinguish true targets from false alarms. Targets, clutter, and noise exhibit different morphological and statistical features in the range-Doppler (R-D) spectrum, and their differences in spatial distribution of echo energy can be described by modeling each detected plot and its surrounding cells as a graph. Based on the above consideration, a novel plot classification network integrating spectrum-to-graph modeling and graph convolutional network (S2G-GCN) is proposed. Firstly, the constant false alarm rate detection algorithm is applied to R-D spectra to obtain potential target plots. For each plot, an echo energy diffusion region is built to include several resolution cells around its spectral peak. Then, these cells are modeled as a graph, where each node corresponds to a cell, and edges are defined using the spatial proximity and energy similarity between neighboring nodes. Finally, a graph convolutional network (GCN)-based classifier is employed to learn discriminative features from the constructed graph and classify each detected plot into one of four classes: true target, sea clutter, ground clutter, or noise. Experimental results demonstrate that the proposed S2G-GCN outperforms three baseline methods, achieving a plot classification accuracy of 93.68%.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".