Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach

In this paper, a novel polarimetric synthetic aperture radar (PolSAR) image classification method based on multilayer autoencoders and self-paced learning (SPL) is proposed. The multilayer autoencoders network is used to learn the features, which convert raw data into more abstract expressions. Then, softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. When we optimize the multilayer autoencoders network, self-paced learning is used to accelerate the learning convergence and achieve a stronger generalization capability. Under this learning paradigm, the network learns the easier samples first and gradually involves more difficult samples in the training process. The proposed method achieves the overall classification accuracies of 94.73%, 94.82% and 78.12% on the Flevoland dataset from AIRSAR, Flevoland dataset from RADARSAT-2 and Yellow River delta dataset, respectively. Such results are comparable with other state-of-the-art methods.