Deep nonsmooth nonnegative matrix factorization network with semi-supervised learning for SAR image change detection

In the paper, we propose a deep nonsmooth nonnegative matrix factorization (nsNMF) network with semi-supervised learning for synthetic aperture radar (SAR) image change detection. In most of the existing deep-NMF-based models, the nonnegative matrix is linearly decomposed layer by layer, which may fail to characterize the nonlinearities in complex data. As such, a nonlinear deep nsNMF model is first built for learning hierarchical, nonlinear, and localized data representations. Meanwhile, in view of its good generalization performance and low computational complexity, extreme learning machine (ELM) is integrated into the nonlinear deep nsNMF model to construct a deep nsNMF network for satisfactory classification. More importantly, since it is difficult to acquire more labeled samples in practice, semi-supervised learning strategy is proposed to make use of partially labeled data for training. The learning process of the proposed network consists of pretraining stage and fine-toning stage, in which the former pretrains all decomposed matrices layer by layer and the latter aims to reduce the total reconstruction error by using the mini-batch gradient descent algorithm. The experimental results on four pairs of SAR images demonstrate the effectiveness of the proposed method.