Texture Features and Unsupervised Learning-Incorporated Rain-Contaminated Region Identification From X-Band Marine Radar Images
Bibliographic record
Abstract
Abstract A novel method is proposed for identifying rain-contaminated regions in X-band marine radar images. Due to the difference of texture between rain-contaminated and rain-free echoes, a Gabor filter bank and discrete wavelet transform (DWT) are introduced to filter marine radar images and generate texture features. Feature vectors extracted from each pixel of the training samples are input into a clustering model, which is trained using unsupervised learning techniques such as k -means and a self-organizing map (SOM). After distinguishing between rain-free and rain-contaminated clusters, the proposed method is able to cluster pixels into rain-free and rain-contaminated types automatically. Images collected from a shipborne marine radar in a sea trial off the east coast of Canada under rain conditions are utilized to validate the proposed method. Identification results obtained from several clustering models with different combinations of cluster number, texture features, and clustering methods show that rain-contaminated pixels are effectively detected, with an overall identification accuracy of 89.1% for both k -means‐based ( k = 4) and 2 × 2-neuron SOM-based clustering models.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".