Sparse Feature Clustering Network for Unsupervised SAR Image Change Detection

In this article, we propose a sparse feature clustering network (SFCNet) for change detection in synthetic aperture radar (SAR) images. One of the principal problems in dealing with SAR images is to reduce the impact of speckle noise. Therefore, based on a neural network framework for change detection, we introduce the multiobjective sparse feature learning (MO-SFL) model where the sparsity of representation is adaptively learned in order to increase the robustness to different levels of noise. For learning the semantic information of changed and unchanged pixels, the network is fine-tuned by the correctly labeled samples selected from coarse results. The selection criterion influences the change detection result a lot. Therefore, we construct a novel cross-entropy clustering loss (CEC) by introducing a clustering regularization term to learn the discriminative representations. Experiments on simulate and real SAR images demonstrate the superiority of the proposed method over compared methods.