On the Impact of Image Pixel Correlation on Multilook SAR Vessel Detection
Bibliographic record
Abstract
This letter is the first to analyze the impact of image pixel correlation in combination with sea surface texture on the detectability of vessels in synthetic aperture radar (SAR). The multilook intensity probability density function (pdf) changes considerably when the pixels are not statistically independent. The pdf tail becomes elongated. The letter investigates theoretically and empirically the deviation between the multilook intensity histogram and various model pdfs when pixel correlation occurs in both calm (homogeneous) and rough (heterogeneous) sea surfaces, respectively. In the literature, it had been concluded that the classic gamma distribution (GD), when using an effective number of looks, fits the data sufficiently well. This, however, is shown to be only true for the bulk of the pdf, whereas the tail can divert significantly from the histogram. As a consequence, the required detection threshold becomes compromised, either causing more false alarms or missing true vessels. It is further demonstrated that a recently introduced discrete compound model, in contrast to the state-of-the-art K-distribution (KD), is capable of mitigating the problem.
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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".