Self-Supervised SAR Anomaly Detection Guided with RX Detector

Anomaly detection in Synthetic Aperture Radar (SAR) images is an important topic. However, the task is challenging due to the scarcity of anomalous samples and the lack of annotated data, which has led most algorithms in this field to be unsupervised. To address the issue, this article proposes a new loss that adds prior information. One of the main functions of an autoencoder is to reconstruct the input data as accurately as possible after encoding them in a latent vector. The proposed loss function guides the network using the Reed-Xiaoli (RX) detector and replaces any pixels in the input data deemed too abnormal with normal surrounding values. This approach incorporates a priori information in addition to the assumption that anomalies are largely under-represented compared to the rest of the image. An ablation study demonstrates that the proposed loss function improves detection performance.