Learning Rotation Domain Deep Mutual Information Using Convolutional LSTM for Unsupervised PolSAR Image Classification

Abstract

Deep learning can archive state-of-the-art performance in polarimetric synthetic aperture radar (PolSAR) image classification with plenty of labeled data. However, obtaining large number of accurately labeled samples of PolSAR data is very hard, which limits the practical use of deep learning. Therefore, unsupervised PolSAR image classification is worthy of further investigation that is based on deep learning. Inspired by the superior performance of deep mutual information in natural image feature learning and clustering, an end-to-end Convolutional Long Short Term Memory (ConvLSTM) network is used in order to learn the deep mutual information of polarimetric coherent matrices in the rotation domain with different polarimetric orientation angles (POAs) for unsupervised PolSAR image classification. First, for each pixel, paired “POA-spatio” samples are generated from the polarimetric coherent matrices with different POAs. Second, a special designed ConvLSTM network, along with deep mutual information losses, is used in order to learn the discriminative deep mutual information feature representation of the paired data. Finally, the classification results can be output directly from the trained network model. The proposed method is trained in an end-to-end manner and does not have cumbersome pipelines. Experiments on four real PolSAR datasets show that the performance of proposed method surpasses some state-of-the-art deep learning unsupervised classification methods.