Classification of ground deformation using sentinel-1 persistent scatterer interferometry time series
| Autor: | Mirmazloumi S.M., Wassie Y., Navarro J.A., Palamà R., Krishnakumar V., Barra A., Cuevas-González M., Crosetto M., Monserrat O. |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | GISCIENCE & REMOTE SENSING r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) Universitat Oberta de Catalunya (UOC) |
| ISSN: | 1548-1603 |
| DOI: | 10.1080/15481603.2022.2030535&partnerID=40&md5=dffb2a2e38a0093cc8cb02a5313c7597 |
| Popis: | Displacement time series (TS) provides temporal and spatial information related to ground deformation. This study aims to investigate temporal behavior of ground deformation TS, including classification of displacement trends and periodicity evaluation, which ease the interpretation of movements. To this end, we propose several modifications to an existing automatic classification workflow of Persistent Scatterers Interferometry (PSI) TS using new tests to classify ground deformations into seven main trends: Stable, Linear, Quadratic, Bilinear, Phase Unwrapping Errors (PUE), Discontinuous with constant and different velocities. We illustrate our approach over 1500 km2 of the Granada region and the metropolitan area of Barcelona, which were monitored using Sentinel-1 images and a PSI technique. This study provided the spatial distribution of different ground movement types and was useful to detect several TS anomalies due to PUE. The proposed approach also identified stable targets, which were wrongly classified as moving scatterers by the existing classification method. A periodicity analysis was finally performed using the Welch’s power spectral density estimator to investigate seasonal and yearly fluctuations. The method was validated using simulated data, where the classified TSs characterized by probable phase unwrapping errors were verified by PSI experts. The overall classification accuracy was 77.8%, indicating that the proposed method has a considerable TS classification potential. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. |
| Databáze: | OpenAIRE |
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