Improvements to Automated Change Detection Tools for SAS Images
Bibliographic record
Abstract
The process of reviewing seabed survey imagery for defence applications is time consuming for naval operators, and this workload is expected to increase with the large data volumes generated as high resolution synthetic aperture sonar (SAS) comes into service. Change detection is a means of reliably detecting newly occurring, moved, or removed seabed objects. It is particularly effective in cluttered environments such as harbours, or in areas that are routinely surveyed. While this technique offers a robust solution to the workload problem, in-service operator tools for post-mission analysis of survey imagery that are specifically designed for change detection are lacking. Coherent change detection (CCD), operating on complex-valued SAS imagery, offers the possibility of detecting very subtle seabed changes, and is an area of current research interest that is not yet implemented in operational tools. Automated change detection (ACD) processing performed on-board the sonar platform during the survey will provide the largest operational utility.Recognizing the complementary aspects of the three nations’ naval research programs in this area, the US (NSWC-PCD, ARL Penn State), Canada (DRDC), and Norway (FFI) have formed a collaborative project to advance operator aids for change detection: Coalition Underwater Mine and IED Defeat (CUMID). Goals of the joint program are to: improve robustness of image-based ACD algorithms; draft requirements and specifications for ACD performance assessment, including both evaluation of past or ongoing performance and prediction of future performance; and architect operator displays, tools, and decision aids. The envisioned final output of the collaboration is a set of well-crafted requirements and recommendations that can be implemented in a manner fitting national priorities and capabilities.This paper provides an overview of current change detection practice (the state of the art), the coalition project, activities ongoing in the participating nations’ research programs, highlights of workshop outputs, and plans for the future.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".