Ship detection and property extraction in radar images using hardware
Bibliographic record
Abstract
In this work we investigate radar imaging satellites' dependency on ground stations to transfer the image data. Since synthetic aperture radar images are very big, only ground stations are equipped to process that much data in realtime. This is a problem for maritime surveillance as it creates delay between imaging and processing. We propose a hardware algorithm that can be used by a satellite to detect ships and extract information about them in real time, and since this information is smaller it can be relayed with significant reduction in delay. For ship detection, adaptive thresholding algorithm with exponential model is used. This algorithm was selected as it can be applied in real time. For the property calculation, a data accumulating, single-look, connected component labeling algorithm is proposed. This algorithm accumulates data about the connected components which is then used to calculate the properties of ships using image moments. The combined algorithm was then validated on RADARSAT-2 images using Matlab for software and co-simulation for hardware. The algorithm was able to detect ships and calculate the features with less than 5% error.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".