An End-to-End Clustering Framework Based on Dynamic Threshold for SAR Images

Manually labeling SAR images is an extremely important but expensive task. Clustering SAR images without labels can effectively reduce the cost of labeling. The existing methods often fail to obtain the optimal solution because the combination of feature representation and clustering assignment, as well as the mining of various correlations behind the input image are ignored. To tackle these problems, an end-to-end deep clustering framework based on a dynamic threshold for SAR images is proposed. In this method, clustering is transformed into a binary pairwise classification problem, and the positive and negative image pairs with high confidence are selected gradually through a dynamic threshold to guide the network representation learning. Various correlations of images are mined and used to learn the most comprehensive representation by reconstructing and maximizing the mutual information between the deep and shallow layers of the network. Extensive experiments on MSTAR datasets show that this method achieves better clustering performance than some state-of-the-art methods.