Classification of polarimetric SAR images using Support Vector Machines
Bibliographic record
Abstract
R. Shah Hosseinia*, I. Entezaria, S. Homayounia, M. Motaghab & B. Mansourica Department of Geomatics and Surveying Engineering, College of Engineering, University of Tehran, Iranb Department of Geodesy and Remote Sensing, Helmholtz Center Potsdam, GFZ German Research Center for Geosciences, Potsdam, Germanyc International Institute of Earthquake Engineering and Seismology, Tehran, Iran Corresponding author: R. Shah Hosseini, (e-mail: rshahosseini@ut.ac.ir). Recently, Support Vector Machines (SVMs) have been introduced as a promising tool for performing supervised classification. This approach has been applied in different contexts and applications, such as data mining, regression analysis, and the classification of remotely sensed data. The advantage of SVMs for data classification is their ability to be used as an efficient algorithm for nonlinear classification problems, particularly in the case of extracting feature vectors from fully polarimetric SAR data. In this research, a classification algorithm based on the SVMs technique is applied to the fully polarimetric AIRSAR L-band data from the San Francisco Bay area, with a spatial resolution of 10 m. Several parameters are extracted from SAR data, including the individual channel backscatter value, Pauli decomposition coefficients, Krogager decomposition coefficients, and eigenvector decomposition parameters. Different combinations of polarimetric parameters are considered to assess the accuracy of the classification results. The accuracy of the SVMs is then compared with that obtained from several conventional classifiers, including the Maximum Likelihood classifier, Minimum Distance classifier, Mahalanobis Distance classifier, and Wishart classifier. The accuracy analysis shows that, for classification of fully polarimetric data, SVMs perform more poorly than the Wishart classifier by approximately 16%, whereas they perform better than the Maximum Likelihood, Minimum Distance, and Mahalanobis Distance classifiers by approximately 4%, 17% and 14%, respectively. Moreover, the highest accuracy is achieved by using the coefficients of Krogager decomposition in the classification procedure. This evaluation demonstrates that the SVM classifier can be used as an effective method for analyzing fully polarimetric SAR images with acceptable levels of accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".