Discriminant deep belief network for high-resolution SAR image classification

Abstract

Classification plays an important role in many fields of synthetic aperture radar (SAR) image understanding and interpretation. Many scholars have devoted to design features to characterize the content of SAR images. However, it is still a challenge to design discriminative and robust features for SAR image classification. Recently, the deep learning has attracted much attention and has been successfully applied in many fields of computer vision. In this paper, a novel feature learning approach that is called discriminant deep belief network (DisDBN) is proposed to learning high-level features for SAR image classification, in which the discriminant features are learned by combining ensemble learning with a deep belief network in an unsupervised manner. Firstly, some subsets of SAR image patches are selected and marked with pseudo-labels to train weak classifiers. Secondly, the specific SAR image patch is characterized by a set of projection vectors that are obtained by projecting the SAR image patch onto each weak decision space spanned by each weak classifier. Finally, the discriminant features are generated by feeding the projection vectors to a DBN for SAR image classification. Experimental results demonstrate that better classification performance can be achieved by the proposed approach than the other state-of-the-art approaches.