Video SAR Image Fusion Using the Effective Reflection Coefficient
Bibliographic record
Abstract
The video synthetic aperture radar (ViSAR), as a mode for sensing in an ever-wider synthetic aperture by sequentially forming SAR images on a series of contiguous or overlapping sub-apertures, has the promising capability to capture wide-angle scattering behavior of objects. In this paper, a novel method for ViSAR image fusion is proposed. First, the ViSAR imaging mode is analyzed and modeled. Based on this model, the reflection coefficient (RC) is then estimated and the significant RC is detected in each sub-aperture image. These detection results are then fused across sub-aperture images to detect scatterers in the area of interest. The performance of the proposed method is evaluated using a simulated scenario and compared with that of the conventional ViSAR image fusion method based on the generalized likelihood ratio test. Numerical results demonstrate the advantages of the proposed method in capturing the aspect-dependent scattering characteristics as well as the spatial structure of objects.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".