Iceberg and ship detection and classification in single, dual and quad polarized synthetic aperture radar
Bibliographic record
Abstract
Iceberg and ship identification in satellite synthetic aperture radar (SAR) data is an essential part of offering an operational iceberg surveillance program. Identification here refers to detection of ocean SAR targets and then classification of these targets as iceberg, ship, or unknown. Maximizing the detection and minimizing incorrect classification of iceberg and ship targets are required. Because coarser resolution satellite SAR data is at times not as intuitive as satellite optical data for manual human interpreted target classification, this process can be labor intensive, subjective, and error prone. Therefore, it is desired that an automated method for iceberg or ship identification be implemented. The methodology investigated here follows a well known standard in supervised pattern recognition, the maximum likelihood-quadratic discriminant function. The goal here in this thesis is to build class models from known iceberg and ship targets. Each class model is based on features that describe targets such as brightness, texture, and shape. Based on these descriptors as training input into the discriminant functions, future unknown targets can be compared with the class model for best fit. The best fit (or minimum distance) is used to assign class status for these unknown targets. One major consideration when using this type of pattern recognition approach is feature selection. Feature selection is based on the notion that some subset (subspace) of the descriptive metrics will lead to improved classification accuracy when comparing discriminant functions. Sequential forward selection and variants of exhaustive search algorithms are implemented and compared. RADARSAT-1, ENVSIAT AP (HH/HV), and EMISAR SAR iceberg and ship targets are used for algorithm training, feature selection, and performance estimation.
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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 | 0.001 | 0.001 |
| 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 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".