Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart
Hua Wen-qiang Wang Shuang Hou Biao
(Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xi’an 710071, China)
(International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an 710071, China)
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.
滑文强,王 爽,侯 彪. 基于半监督学习的SVM-Wishart极化SAR图像分类方法[J]. 雷达学报, 2015, 4(1): 93-98.
Hua Wen-qiang,Wang Shuang,Hou Biao. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart. JOURNAL OF RADARS, 2015, 4(1): 93-98.
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