Polarimetric SAR image filtering with trace-based partial differential equations
Bibliographic record
Abstract
AbstractPartial differential equations (PDEs) filtering methods provide regularization of an image through successive iterations, where pixel values are gradually diffused in accordance with a local diffusion tensor field. The local diffusion tensor field determines the local smoothing geometry that should drive the regularization process. Most diffusion-based filtering methods rely on a divergence formulation for the diffusion term which does not produce an optimal geometry-preserving regularization. A new trace-based PDE approach was recently proposed and has been applied to the filtering of colour images and other multivalued data. Compared to a classical divergence-based formulation approach, a trace-based formulation better preserves the geometric content of the image. We propose to apply this new framework to the filtering of polarimetric synthetic aperture radar (PolSAR) images. In particular, the calculation of the local geometry is modified to be robust to the speckle noise. Speckle reduction performance is evaluated on both artificial and real PolSAR images and compared with other standard speckle reduction filters in terms of radiometric noise reduction and meaningful details preservation.Les méthodes de filtrage à base d'équations aux dérivées partielles (EDP) permettent de régulariser une image de manière itérative en diffusant graduellement les valeurs des pixels en fonction d'un tenseur de diffusion. Le champ local des tenseurs de diffusion spécifie la géométrie locale du lissage qui contrôle le processus de régularisation. La plupart des méthodes de filtrage par diffusion sont basées sur une formulation en divergence pour le terme de diffusion qui ne produit pas nécessairement une régularisation optimale préservant la géométrie de l'image. Une nouvelle approche EDP basée sur l'opérateur trace a été récemment proposée et appliquée au filtrage des images couleurs et autres données multivariées. Comparativement à l'approche classique à base de divergence, l'approche trace permet une meilleure préservation du contenu géométrique de l'image. Nous proposons d'appliquer ce nouveau cadre théorique au filtrage des images radar polarimétriqes (PolSAR). En particulier, le calcul de la géométrie locale de l'image est modifié afin d'offrir une robustesse au bruit de chatoiement. Les performances, en termes de résolution radiométrique et de préservation des détails significatifs, sont évaluées sur des images radar polarimétriques artificielles et réelles et sont comparées avec celles des filtres de chatoiement standards.
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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 | 0.001 | 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".