Sensors, Vol. 24, Pages 2637: Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms

Sensors, Vol. 24, Pages 2637: Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms Sensors doi: 10.3390/s24082637 Authors: Lama Moualla Alessio Rucci Giampiero Naletto Nantheera Anantrasirichai Monitoring ground displacements identifies potential geohazard risks early before they cause critical damage. Interferometric synthetic aperture radar (InSAR) is one of the techniques that can monitor these displacements with sub-millimeter accuracy. However, using the InSAR technique is challenging due to the need for high expertise, large data volumes, and other complexities. Accordingly, the development of an automated system to indicate ground displacements directly from the wrapped interferograms and coherence maps could be highly advantageous. Here, we compare different machine learning algorithms to evaluate the feasibility of achieving this objective. The inputs for the implemented machine learning models were pixels selected from the filtered-wrapped interferograms of Sentinel-1, using a coherence threshold. The outputs were the same pixels labeled as fast positive, positive, fast negative, negative, and undefined movements. These labels were assigned based on the velocity values of the measurement points located within the pixels. We used the Parallel Small Baseline Subset service of the European Space Agency’s GeoHazards Exploitation Platform to create the necessary interferograms, coherence, and deformation velocity map...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research