Bathymetric Feature Extraction and Matching on Semi-Synthetic Sonar Imagery
Bibliographic record
Abstract
Feature extraction and matching is critical in land-mark association to aid autonomous underwater vehicle (AUV) navigation. Typical feature extraction methods are designed for optical imagery and are not optimized for underwater bathymetry. In this paper, a method of generating semi-synthetic bathymetry sonar images is presented, as a precursor to de-risk sea trials. These sonar image datasets are used to evaluate feature matching performance of feature detectors and descriptors commonly applied to sonar imagery. In these experiments, combinations of the BRISK, FREAK, SIFT and SURF algorithms are parameter-tuned and evaluated to determine the optimal detector-descriptor configurations for bathymetric feature ex-traction. Feature matching is reported using a bathymetric map of Delaware Bay [1] and a semi-synthetic sonar image dataset. Feature extraction and matching performance is evaluated using precision, recall, and the number of true positive matches be-tween features detected in the semi-synthetic sonar imagery and features detected in the full bathymetric map. SIFT was found to be a robust bathymetric feature detector. For the conditions used in this research, the SIFT detector combined with the BRISK descriptor was shown to be the best detector-descriptor combination for bathymetric feature extraction. Matching SIFT_BRISK features achieved perfect precision and perfect recall on 53 correct feature matches.
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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 | 0.003 |
| 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 | 0.000 | 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".