Multiview Visual and Topological Features Coordination Aggregation Framework for SAR Target Recognition
Bibliographic record
Abstract
Due to the extensive information in multi-view images, multi-view synthetic aperture radar (SAR) automatic target recognition (ATR) has attracted much attention. However, most current algorithms ignore the integration of multi-view topological features intrinsically related to the characteristics of SAR images. Moreover, as well as not thoroughly exploring the inherent coupling relationship, the multi-view feature fusion modules in these algorithms are also prone to cause the serve parameter burden and insufficient generality of the entire ATR model. To tackle these issues, an ATR model called multi-view visual and topological feature coordination aggregation (MVT-CA) is proposed. First, two parallel feature extraction modules are employed to extract multi-view visual and topological features independently. Specifically, the topological feature extraction module (TFM) based on a hypergraph neural network is designed to extract the implicit topological features by aggregating the context refinement features of key points within the SAR images. Subsequently, a parameterfriendly feature coordination aggregation module (FCAM) with visual and topological consistency is introduced, which effectively integrates multi-view features to generate a unified representation for classification while enhancing the generality of the entire ATR model. Experimental results on the Moving and Stationary Target Recognition (MSTAR) and the Full Aspect Stationary TargetsVehicle (FAST-Vehicle) datasets verify the effectiveness of our MVTCA model, even in scenarios involving severe background noise and target deformation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".