Sublook Contrastive Learning for SAR Representation Learning and Image Classification

Deep learning networks, such as convolutional neural networks (CNNs), are increasingly applied to synthetic aperture radar (SAR) feature representation and image classification. However, the performance of most deep learning methods relies on sufficient labeled data, which is difficult to collect for SAR images. As a result, self-supervised contrastive learning (CL) methods have attracted enthusiasm in recent studies to learn SAR representations with unlabeled data. Most existing CL-based methods for SAR representation learning simply use the amplitude information as the network input but neglect the particular features of complex-valued SAR images such as spectral information, leading to insufficient feature extraction. To address this issue, we propose a self-supervised sublook CL (SCL) method to learn spectral representations from unlabeled data. First, we extract sublooks from SAR images to establish a spectral representation branch (SRB) to discover the spectral information. Second, a novel SCL method with a sublook-matching task is proposed based on a contrastive network to learn spectral representations. The branch is applied to SAR image classification tasks by integrating it with a typical amplitude network through feature concatenation. Experimental results have validated the effectiveness and generalization ability of the proposed method with a different experimental protocol that distinguishes the pretrain and downstream datasets.