Multiscale classification and filtering of SAR images using Dempster-Shafer theory
Bibliographic record
Abstract
Classification of high resolution SAR images is difficult due to the presence of speckle noise. We propose to use a multiscale decomposition that allows different trade-off between spatial precision (resolution) and radiometric uncertainty (noise reduction). Classification decisions at large scale are certain but spatially imprecise whereas decisions at high resolution are uncertain but spatially precise. We first decompose the SAR image in low and high frequency images at different scales using a stationary wavelet transformation. Then low pass images are classified by maximum likelihood based on a Gaussian mixture estimation. Wavelet coefficients in high frequency images enable us to identify stationary homogeneous regions within the image where classification decisions are expected to be stable across scales. Decisions at different scales are merged using Dempster-Shafer theory which gives us an adequate framework to manipulate both uncertainty and imprecision. Finally, resulting multiscale decisions are injected in a stochastic classification algorithm (MPM) as a hidden "evidential" Markov random field. The proposed algorithm is evaluated on artificial SAR images. We also propose to filter wavelet coefficients based on the resulting multiscale confidence map.
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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 | 0.000 |
| 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 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".