A Novel PolSAR Image Classification Method Based on Optimal Polarimetric Features and Contextual Information
Bibliographic record
Abstract
Due to the severe speckle noise of a fully polarimetric synthetic aperture radar image and the complex backscattering mechanism at the junction of different land covers, some of the pixels are easily mislabeled, especially on the edge of the land covers. To address this issue, this study presents a novel scheme that selects polarimetric features step by step to participate in classification through different classification mechanisms. Different from previous classification methods where all land covers are described by the same polarimetric features, we make a fine selection of polarimetric features for each land cover with the help of information entropy and contextual information. Among many polarimetric features, elements of the covariance matrix and the coherence matrix are selected to optimize the original classification results. The experimental results show that the proposed method can achieve good classification results, especially in the edge area of land covers. Compared to traditional classification methods, the proposed method is robust and is able to improve the overall accuracy by more than 5.58% and the kappa coefficient by more than 0.0613.
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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".