부산대학교 도서관

Effective feature representation is the key to synthetic aperture radar (SAR) image terrain classification. Limited by the abstract appearance and the scarcity of high-quality labeled data in this field, the features learned by current methods, especially deep learning models, do not have enough directivity and applicability, which hampers the performance. This article proposes multi-image factor self-supervised learning (MFSSL) to achieve directional feature learning and obtain generalized features with few patch-level labeled data. The framework consists of an upstream multifactor image style transfer task and a downstream terrain classification task. In the upstream task, the goal of feature learning is first set up by multiple SAR image factors, including the observation region, the terrain category, and the imaging parameters. Then, different styles of SAR terrain images are generated and reconstructed under this goal. Through this bidirectional generative learning, the low-level external appearance of the terrain is removed, while the essential and discriminative feature representation is retained and shared across different factors. Finally, the downstream model inherits the general feature from the upstream model and implements the terrain classification task using a small amount of labeled data. Experiments conducted on three broad SAR scenes with different image factors demonstrate that the proposed framework can improve pixel-level terrain classification only with a few patch-level labeled data.