Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

Abstract

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.

Keywords

• 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.