Rethinking the phase in single-channel SAR imagery
Bibliographic record
Abstract
Focused single-channel synthetic aperture radar (SAR) imagery is a complex-valued. The common practice in the radar community is to entirely discard the phase information and to only use the detected intensity or magnitude image. This practice hinges on the hypotheses of the so-called fully-developed speckle model which is no more applicable to sub-meter high-resolution SAR imagery. Through properly utilizing the phase information content, not only the spatial resolution present in the complex-valued SAR image is retained, but also the accuracy of detection and recognition of targets can be potentially boosted. Two main contributions are presented in this paper. First, a novel method termed backscatter relative phase image (BRPI) is proposed to characterize the phase content in single-channel SAR imagery. Second, the von Mises (vM) distribution is adopted as a new statistical model for modeling the characteristic phase. The applicability of our proposed algorithm is demonstrated on ground-truthed Radarsat-2 civilian vehicle targets. The proposed method is straightforward to use with any complex-valued high-resolution radar data other than SAR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".