InSAR deformation time series classification using a convolutional neural network

Abstract

Temporal analysis of deformations Time Series (TS) provides detailed information of various natural and humanmade displacements. Interferometric Synthetic Aperture Radar (InSAR) generates millimetre-scale products, indicating the chronicle behaviour of detected targets via TS products. Deep Learning (DL) can handle a massive load of InSAR TS to categorize significant movements from non-moving targets. To this end, we employed a supervised Convolutional Neural Network (CNN) model to distinguish five deformations trends, including Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error (PUE). Considering several arguments in a CNN model, we trained numerous combinations to explore the most accurate combination from 5000 samples extracted from a Persistent Scatterer Interferometry (PSI) technique and Sentinel-1 images over the Granada region, Spain. The model overall accuracy exceeds 92%. Deformations of three cases of landslides were also detected over the same area, including the Cortijo de Lorenzo, El Arrecife, and Rules Viaduct areas.