Statistical Anomaly Detection Using Persistent Scatterer Interferometry

This paper proposes a statistical anomaly detection method using persistent scatterer interferometry (PSI) . Change detection methods utilizing phase information in synthetic aperture radar (SAR) images are sensitive to various changes, including regular changes such as orbital changes, slow displacements, atmospheric effects, and observation noises, making it difficult to detect true anomalies. To address this issue, the proposed method employs the PSI results to construct a statistical model of regular changes, which can predict a new image. Anomalies are identified as pixels in the actual image that exhibit significant deviations from the prediction. The effectiveness of the proposed method is demonstrated through its application to real SAR data, where the potential damaged areas are easily discernible.