CFAR ship detection system using polarimetric data
Bibliographic record
Abstract
The objective of this work is to develop optimal polarimetric Constant False Alarm Rate (CFAR) ship detection system. Polarimetric transformation/decompositions, clutter analysis, modeling, Principal Component Analysis (PCA), and multi-CFAR detection are the necessary components of optimal polarimetric CFAR ship detectors. The resulting CFAR detector outperforms the conventional polarimetric CFAR detector by providing higher probability of detection without introducing much more computations. Optimal polarimetric CFAR detection procedures are proposed in this report. Given the simulated polarimetric RADARSAT-2 data, different polarimetric transformations and decompositions are applied. The resulting images will be transmitted to an adaptive Principal Component Analysis (PCA) block. Through the adaptive PCA block, the image of the first principal component which has the highest SNR among all the images (including the original, transformed/decomposed images, and the images after the adaptive PCA) will be used for ship detection. Optimal multi-CFAR detection will be applied to this image and then the final decision will be made.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".