Neural Network Pattern Recognition Experiments Toward a Fully Automatic Detection of Anomalies in InSAR Time Series of Surface Deformation - Milillo, Pietro; Sacco, Gianfranco; Di Martire, Diego; Hua, Hook (Frontiers in Earth Science, 2022)
We present a neural network-based method to detect anomalies in time-dependent surface deformation fields given a set of geodetic images of displacements collected from multiple viewing geometries. The presented methodology is based on a supervised classification approach using combinations of line of sight multitemporal, multi-geometry interferometric synthetic aperture radar (InSAR) time-series of displacements. We demonstrate this method with a set of 170 Million time-series of surface deformation generated for the entire Italian territory and derived from ERS, ENVISAT, and COSMO-SkyMed SAR satellite constellations. We create a training dataset which has been compared with independently validated data and current state of the art classification techniques. Compared to state-of-the-art algorithms, the presented framework provides increased detection accuracy, precision, recall and reduced processing times for critical infrastructure and landslide monitoring. This study highlights how the proposed approach can accelerate the anomalous points identification step by up to 147 times compared to analytical and other artificial intelligence methods and can be theoretically extended to other geodetic measurements such as GPS, leveling data or extensometers. Our results indicate that the proposed approach would make the anomaly identification post-processing times negligible when compared to the InSAR time-series processing.