Ship Detection and Classification in EO/IR VHR Satellite Imagery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ship detection and classification in very high resolution (VHR) EO/IR satellite imagery, as primary objectives, were investigated using multiple techniques. Automated algorithms were developed and their performance was evaluated using different satellite image sources (Pleiades, WorldView-2/3). Performance of ship detection algorithms based on traditional (thresholding and saliency) techniques reached probability of detection 80% for low false alarm rates. Deep learning techniques based on convolutional neural networks (CNNs) (YOLOv4 and Mask R-CNN) achieved average precision of 94–95% with 3% of false positives without the need of accurate land and cloud masking. Mask R-CNN also allows accurate determining ship size parameters. The problem of ship and non-ship classification was investigated using traditional and CNN based techniques. Linear Discriminant Analysis, Support Vector Machines and combined classifiers achieved classification accuracies close to 80–90%. At the same time, the usage of a technique based on GoogleNet CNN achieved 99% classification accuracy for ship, small boats and background targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it