A New Image Fusion Method for Ship Target Enhancement in Spaceborne and Airborne SAR Collaboration
Bibliographic record
Abstract
In this paper, we investigate the fusion of spaceborne synthetic aperture radar (SAR) and airborne SAR images and its application to ship target enhancement. In this paper, we propose a new target proposal and clutter copula (TPCC)-based image fusion method for the collaboration of spaceborne and airborne SARs. TPCC enhances the common ship target areas in spaceborne and airborne SAR images via the intersection of target proposals and suppresses the clutter areas by establishing the joint distribution of clutter in the spaceborne and airborne SAR images based on the copula theory. Compared with other commonly used image fusion methods, the target dependence and clutter dependence in the spaceborne and airborne SAR images are newly exploited in TPCC. We demonstrate the superiority of TPCC in terms of target-to-clutter ratios (TCRs) by using composite images combining Gaofen-3 satellite and unmanned aerial vehicle (UAV) SAR images.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".