Unreliable Pixel Contrast Based on von Mises-Fisher Distribution for Semi-Supervised SAR Segmentation

The unique visual properties and the huge size of synthetic aperture radar (SAR) images pose challenges in labeling data. The scarcity of labeled data limits the training of SAR image segmentation networks. To address this issue, semi-supervised methods are used to train the network. However, traditional semi-supervised algorithms, such as Mean Teacher, often generate erroneous pseudo-labels that carry over to subsequent training epochs, leading to overfitting and affecting network performance. This overfitting stems from an overreliance on unreliable pixels in Mean Teacher. In this study, an enhanced approach is proposed called unreliable pixel contrast (UPCo), where a von Mises-Fisher (vMF) distribution is applied to constrain unreliable pixels in feature space. We augment the segmentation network with a feature output header for pixel-level contrastive learning in UPCo. Moreover, to minimize the computational effort during the training phase, hard sample selection (HSS) and negative object nonuniform selection (NONS) strategies are designed to facilitate contrastive learning. The proposed UPCo was evaluated on two large-scene SAR images and demonstrated its superiority over other comparative algorithms, achieving more optimal performance in semi-supervised segmentation of SAR images.