When PolSAR image using the existing deep learning model for classification, it rarely considers the character of PolSAR data which is complex-valued. Besides, deep learning needs a large number of labeled samples, while the obtained labeled samples of PolSAR image are very limited. In view of these problems, this paper proposed a semi-supervised PolSAR image classification which combined the Tri-training and CV-CNN. Firstly, it obtained some pseudo-label samples with high reliability through the Wishart classifier and Tri-training. Then, the CV-CNN model training process used the obtained pseudo-label samples with some labeled samples. Finally, the image classification task was completed. Simulation experiments of four PolSAR image show that the proposed method not only can effectively improve the reliability of pseudo-label samples, but also can improve the classification accuracy.

[2021-08-05] Printed Article /doc/2020.11.0430/v2