Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart
| Autor: | Hua Wen-qiang, Wang Shuang, Hou Biao |
|---|---|
| Jazyk: | English<br />Chinese |
| Rok vydání: | 2015 |
| Předmět: |
In this study
we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR) images aiming at handling the issue that the number of train set is small. First considering the scattering characters of PolSAR data this method extracts multiple scattering features using target decomposition approach. Then a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM). Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small. Electricity and magnetism QC501-766 |
| Zdroj: | Leida xuebao, Vol 4, Iss 1, Pp 93-98 (2015) |
| Druh dokumentu: | article |
| ISSN: | 2095-283X |
| DOI: | 10.12000/JR14138 |
| Popis: | In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR) images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM). Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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