Semisupervised Deep Convolutional Neural Networks Using Pseudo Labels for PolSAR Image Classification
Zheng Fang, Gong Zhang, Qijun Dai, Yingying Kong, Peng Wang
Abstract
Deep-learning-based methods have obtained satisfying results in polarimetric synthetic aperture radar (PolSAR) image classification. However, these methods require large numbers of labeled samples, which are usually time-consuming and high-priced for PolSAR images. To address this issue, a semisupervised method based on a 3-D convolutional neural network (3-D-CNN) using pseudo labels (PL-3-D-CNN) is proposed. First, the coherency matrix of PolSAR data is converted into a 6-D real-valued vector by a unitary transformation. Then, the K-means algorithm is utilized for generating pseudo labels. After that, labeled samples and pseudo labeled samples are fed into the PL-3-D-CNN model to extract supervised and unsupervised features. Finally, the supervised and unsupervised features are combined to improve classification accuracy. The proposed method is tested on both AIRSAR and RADARSAT-2 data sets. The results show that the proposed method is an effective method for PolSAR image classification and shows good performance under a small number of labeled samples. The source code for the PL-3-D-CNN model is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/fangzheng-nuaa/PL-3D-CNN</uri> .