Extraction of Urban Areas From Polarimetric SAR Imagery
Bibliographic record
Abstract
Polarimetric synthetic aperture radar (PolSAR) images are extensively used for land-use/land-cover (LULC) classification. One of the important issues in radar remote sensing is urban area detection, where difficulties are found because of its heterogeneity. In this paper, we are interested in urban area detection using PolSAR images which allow us detecting the scattering mechanisms by the use of polarimetric target decompositions methods. We propose in this paper two methods: in the first one, we use the powers of Yamaguchi four-component decomposition and in the second method, we use the coefficients of PolSAR covariance matrix calculated in the circular polarization basis. We added in each method the complex Wishart maximum likelihood (ML) classifier to refine the classification results. To validate both methods, we used two PolSAR images acquired in C-band by RADARSAT-2 satellite over the El Hamiz city in Algeria and San Francisco Bay. The two proposed algorithms give accurate results in both test sites, with superiority of the circular condition method.
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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 | 0.000 | 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".