INSAR DEFORMATION TIME SERIES CLASSIFICATION USING A CONVOLUTIONAL NEURAL NETWORK

6 citations as recorded by crossref.

1. Improving Ground Deformation Classification by Integrating InSAR Time Series With Geospatial Information Y. Dong et al. https://doi.org/10.1109/TGRS.2025.3635271
2. Optical–SAR and GIS fusion with physics-informed deep learning for enhanced InSAR-based deformation monitoring in hazard-prone environments H. Omar et al. https://doi.org/10.1007/s11069-026-08315-2
3. Estimating the period of probable landslide event using advanced D-InSAR technique for time-series deformation study of Kotrupi region K. Niraj et al. https://doi.org/10.1080/19475705.2023.2281245
4. Intelligent detection of IT assets in power grid enterprises based on signal processing and cyberspace Y. Zhang https://doi.org/10.3233/JCM-226552
5. SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring D. Roque et al. https://doi.org/10.3390/su15043728
6. A Deep-Learning-Facilitated, Detection-First Strategy for Operationally Monitoring Localized Deformation with Large-Scale InSAR T. Wang et al. https://doi.org/10.3390/rs15092310