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Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 307–312, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-307-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 307–312, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-307-2022
 
30 May 2022
30 May 2022

INSAR DEFORMATION TIME SERIES CLASSIFICATION USING A CONVOLUTIONAL NEURAL NETWORK

S. M. Mirmazloumi1, Á. F. Gambin2, Y. Wassie1, A. Barra1, R. Palamà1, M. Crosetto1, O. Monserrat1, and B. Crippa3 S. M. Mirmazloumi et al.
  • 1Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Geomatics Research Unit, Av. Gauss, 7, E-08860 Castelldefels (Barcelona), Spain
  • 2Artificial Intelligence Lab, Oslo Metropolitan University, Oslo, Norway
  • 3Dept. of Geophysics, University of Milan, Via Cicognara 7, I-20129 Milan, Italy

Keywords: SAR, CNN, Deformation Time Series, Persistent Scatterer Interferometry, Sentinel-1

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.