Deep Dictionary Pair Learning for SAR Image Classification

Projective dictionary pair learning (DPL) provides an effective solution to the image classification problem by jointly learning two dictionaries, i.e., the synthesis dictionary and the analysis dictionary, for the purpose of image representation and discrimination. However, the DPL algorithm focuses only on dictionary learning, ignores the importance of feature learning. Therefore, we propose a new deep dictionary pair learning (DDPL) network that combines feature learning and dictionary learning in an end-to-end architecture. Specifically, the DPL approach is embedded in a deep convolutional neural network (DCNN) by introducing two dictionary learning layers. In other words, the DCNN is used to learn high-quality and appropriate image features, while the DPL uses the learned deep features for dictionary learning and guides the update of the deep network. Finally, our network architecture is trained by a backpropagation algorithm that minimizes the standard deep dictionary pair learning loss function, which is simpler than the traditional alternating direction method of multipliers (ADMM) optimization algorithm. Experimental results on three SAR image classification datasets show that our approach significantly outperforms some state-of-the-art SAR classification methods in terms of classification accuracy.