Graph-Based Anomaly Detection and Localization in InSAR Data

The generation of ground-movement maps using interferometric SAR is subject to faults during the acquisition and prepossessing of SAR data. This paper addresses the task of identifying and localizing such faults specifically within the context of data provided by the European Ground Motion Service (...

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Bibliographic Details
Published inIEEE International Geoscience and Remote Sensing Symposium proceedings pp. 7405 - 7409
Main Authors Elias, Vitor R. M., Dehls, John, Rossi, Pierluigi Salvo
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.07.2024
Subjects
Distributed databases
Fault detection
Filters
Geospatial analysis
graph
InSAR
Location awareness
Machine learning
Training
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Summary:The generation of ground-movement maps using interferometric SAR is subject to faults during the acquisition and prepossessing of SAR data. This paper addresses the task of identifying and localizing such faults specifically within the context of data provided by the European Ground Motion Service (EGMS). The lack of a regular spatial domain in geospatial data poses a challenge for signal processing and machine learning. Here, we investigate graph-based methods, that overcome the spatial irregularity of the data, to perform detection and localization of data anomalies. We demonstrate on synthetic data that graph-based frequency analysis and filtering yield superior performance at anomaly localization when compared to state-of-the-art machine-learning methods such as graph U-nets. The proposed methods are employed for anomaly detection and localization on real EGMS data.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10641404