Feature extraction and classification of PolSAR images based on sparse representation
Bibliographic record
Abstract
In this paper, a supervised PolSAR images classification method based on sparse representation is proposed. Firstly, Polarimetric decomposition based on Multiple-component Scattering Model, Cloude-Pottier decomposition and Gray-level Co-occurrence Matrix are implemented to obtain features which can describe PolSAR images at multiple aspects. Then, the training samples are represented in an overcomplete dictionary in which the basic elements are the feature vectors of the training samples and this can be computed by minimize l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -norm. Through comparing the residuals of the reconstructed training samples corresponding to different classes with the original training sample, the optimization problem can be solved and obtain the classification result. The proposed method is validated by the Danish EMISAR L-band fully polarimetric SAR data of Foulum Area (DK) and the preliminary experimental results confirm the performance and potential of the proposed method in PolSAR image interpretation.
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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".