EDLRDPL_Net: A New Deep Dictionary Learning Network for SAR Image Classification

Xizhan Gao, Kang Wei, Sijie Niu, Hui Zhao, Jiwen Dong

Abstract: SAR image classification is one of the research hotspots in the field of remote sensing. The performance of SAR image classification greatly depends on the learning of features and the design of classifiers. However, traditional SAR image classification methods either focus on learning (deep) features or focus on designing discriminative classifiers, while ignoring the correlation between them, resulting in the learned features and classifiers often not matching. To address the above issues, in this letter a novel classifier, termed entropy based low-rank dictionary pair learning (ELRDPL) method is first designed, which introduces the entropy theory and low-rank constraints into the objective function of dictionary learning for increasing the discriminative ability and decreasing the space occupation. Based on the constructed classifier, an entropy based deep low-rank dictionary pair learning network (EDLRDPL_Net) is then proposed, which performs joint learning of deep features and discriminative dictionaries by embedding the ELRDPL classifier into the deep neural network. Extensive experimental results on four SAR image classification datasets demonstrate the effectiveness of EDLRDPL_Net.