Improved Vessel Detection via Quadratic Matched Filtering and Target Parameter Estimation for Dual and Compact Polarimetric SAR
Bibliographic record
Abstract
Dual polarimetric (DP) and compact polarimetric (CP) SAR modes are preferred over quad-channel fully polarimetric (FP) modes for wide-area maritime domain surveillance applications because they provide twice the swath width. Recently, the multilook complex (MLC) product was introduced for RADARSAT Constellation Mission (RCM) imagery, which preserves the polarimetric phase information at a considerably lower data volume over the traditional phase-preserved single-look complex (SLC) product. From statistical theory, the optimal detector for polarimetric data has been previously derived and is known as the optimal polarimetric detector (OPD). However, for a deterministic target model, this detector cannot be applied to MLC data and is also impractical, because it requires complete a priori knowledge of the target. Instead, a suboptimal detector, known as the polarimetric whitening filter (PWF), is often used in practice. This letter proposes a new detector called “quadratic matched filter (QMF)” for CP and DP data that can be applied to MLC products for improved vessel detection over the PWF. A technique to estimate target parameters at processing time is also proposed, which can be used to estimate target parameters for both OPD and QMF. The feasibility and improved performance of the QMF detector are demonstrated through simulated receiver operating characteristic (ROC) performance analysis, and by demonstrating detection performance on an image acquired by RCM. It is shown that the QMF provides approximately 1.5 and 3 dB improvement in signal-to-clutter-plus-noise ratio (SCNR) and peak-signal-to-clutter-plus-noise ratio (PSCNR), respectively, over PWF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".