  In this paper, we proposed to investigate unsupervised anomaly detection in
Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as
abnormal patterns that deviate from their surroundings but without any prior
knowledge of their characteristics. In the literature, most model-based
algorithms face three main issues. First, the speckle noise corrupts the image
and potentially leads to numerous false detections. Second, statistical
approaches may exhibit deficiencies in modeling spatial correlation in SAR
images. Finally, neural networks based on supervised learning approaches are
not recommended due to the lack of annotated SAR data, notably for the class of
abnormal patterns. Our proposed method aims to address these issues through a
self-supervised algorithm. The speckle is first removed through the deep
learning SAR2SAR algorithm. Then, an adversarial autoencoder is trained to
reconstruct an anomaly-free SAR image. Finally, a change detection processing
step is applied between the input and the output to detect anomalies.
Experiments are performed to show the advantages of our method compared to the
conventional Reed-Xiaoli algorithm, highlighting the importance of an efficient
despeckling pre-processing step.
