Kuzu, Ridvan Salih und Bagaglini, Leonardo und Wang, Yi und Dumitru, Corneliu Octavian und Ait Ali Braham, Nassim und Pasquali, Giorgio und Santarelli, Filippo und Trillo, Francesco und Saha, Sudipan und Zhu, Xiao Xiang (2023) Automatic Detection of Building Displacements Through Unsupervised Learning From InSAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, Seiten 6931-6947. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2023.3297267. ISSN 1939-1404.
![]() |
PDF
- Verlagsversion (veröffentlichte Fassung)
10MB |
Offizielle URL: https://ieeexplore.ieee.org/abstract/document/10188664
Kurzfassung
We introduce an unsupervised learning method that aims to identify building anomalies using Interferometric Synthetic Aperture Radar (InSAR) time-series data. Specifically, we leverage data obtained from the European Ground Motion Service to develop our proposed approach, which employs a long short-term memory autoencoder model and a reconstruction loss function based on a soft variant of the dynamic time warping, namely “soft-DTW”. We deliberately utilize this loss function for its ability to compare time-series that are not aligned in time, unlike the other conventional reconstruction losses that do not account for time shifts. Moreover, we enhance the performance of anomaly detection by smoothing inputs with a Hann window and defining the learning objective to reconstruct the time order of randomly permuted input series. Our experimental findings, based on persistent scatterer data from Rome, Italy, reveal that our method outperforms several unsupervised machine learning and deep learning methods in detecting various types of building displacement, such as trend, noise, and step anomalies. Additionally, quantitative and qualitative evaluations validate the efficacy of our approach in identifying potentially anomalous buildings. Thus, our method offers a promising solution for detecting anomalies in PS-InSAR time-series, which could have substantial implications in the fields of urban monitoring and infrastructure management.
elib-URL des Eintrags: | https://elib.dlr.de/198750/ | ||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||||||||||||||||||
Titel: | Automatic Detection of Building Displacements Through Unsupervised Learning From InSAR Data | ||||||||||||||||||||||||||||||||||||||||||||
Autoren: |
| ||||||||||||||||||||||||||||||||||||||||||||
Datum: | 2023 | ||||||||||||||||||||||||||||||||||||||||||||
Erschienen in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | ||||||||||||||||||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||||||||||||||||||
Gold Open Access: | Ja | ||||||||||||||||||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||||||||||||||||||
Band: | 16 | ||||||||||||||||||||||||||||||||||||||||||||
DOI: | 10.1109/JSTARS.2023.3297267 | ||||||||||||||||||||||||||||||||||||||||||||
Seitenbereich: | Seiten 6931-6947 | ||||||||||||||||||||||||||||||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||||||||||||||||||||||||||
ISSN: | 1939-1404 | ||||||||||||||||||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||||||||||||||||||
Stichwörter: | Buildings, Deformation, Time series analysis, Monitoring, Training, Market research, Unsupervised learning | ||||||||||||||||||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - SAR-Methoden, R - Künstliche Intelligenz | ||||||||||||||||||||||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||||||||||||||||||||||||||
Hinterlegt von: | Kuzu, Dr. Ridvan Salih | ||||||||||||||||||||||||||||||||||||||||||||
Hinterlegt am: | 08 Nov 2023 12:17 | ||||||||||||||||||||||||||||||||||||||||||||
Letzte Änderung: | 27 Feb 2024 18:13 |
Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags