A Study of Selected Constant False Alarm Rate (CFAR) Detectors for Vessels with Compact and Dual Polarimetric Synthetic Aperture Radar (SAR)
Bibliographic record
Abstract
Space-based polarimetric synthetic aperture radar (SAR) plays an important role in wide-area maritime surveillance and vessel detection. Traditionally, dual polarimetric (DP) SAR has been preferred for such applications, but compact polarimetric CP SAR is a newer option that potentially offers improved backscatter characterization of targets. There are also a variety of detectors proposed for vessels in literature that can be applied to both CP and DP SAR. Unfortunately, it is difficult to know which detector should be used in practice. To that end, this paper presents a survey of detectors and their comparative assessment with simulated CP and DP SAR data. All detectors are systematically analyzed within the framework of statistical hypothesis testing. Receiver operating characteristic (ROCs) are then generated via Monte Carlo simulations for performance analysis and comparative assessment of all surveyed detectors in CP and DP data. Results show that statistical detectors derived from the likelihood-ratio test (LRT) tend to outperform other detectors under homogenous interference, but degradation in relative performance of some LRT based detectors is observed when tested with heterogeneous interference. Results also show that CP provides better detection performance than DP in most cases when targets exhibit even-bounce scattering.
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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 | 0.005 | 0.019 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".