Automated detection of migrating Gray whales and measurement of their bearing in satellite imagery
Bibliographic record
Abstract
Abstract Satellite images could aid in studying baleen whale migration routes given their extensive spatial coverage. We developed two object‐based image analysis (OBIA) workflows to automatically detect migrating whales and their orientation. We used three sections of a Worldview‐3 satellite image off California with migrating gray whales (Eschrichtius robustus), alternating training and testing under a three‐fold approach in eCognition, with three classes: whale, water, and confounding objects (e.g., sea foam). After a workflow with 202 classification features, we applied feature selection through a minimum redundancy maximum relevance algorithm and retained 15 features. This selection was used in a second workflow which yielded fewer false negatives (FNs) and especially, fewer false positives (FPs) (22.6% vs. 11.1% FNs rate, 7599.6% vs. 1352.6% FP over detectable rate). While FPs were still considerable, image grid cells to review were reduced to less than 3% of cells for full manual analysis. Using Moore's test for paired circular data, we failed to find significant differences between manual and both automated or semiautomated orientation measurements (corrected for whales measured from head to tail). Most whales were oriented in the southeastern quadrant. The results are promising for satellite image studies of migration routes.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".