Optical–SAR and GIS fusion with deep learning for InSAR coherence enhancement in deformation mapping

Title : Optical–SAR and GIS fusion with deep learning for InSAR coherence enhancement in deformation mapping

Abstract:

The integration of artificial intelligence and remote sensing data into Geographic Information Systems (GIS) is transforming the capabilities of Earth observation and spatial analysis. Within this context, accurate deformation monitoring using Differential Interferometric Synthetic Aperture Radar (DInSAR) remains challenged by spatial and temporal decorrelation in interferograms.

Recent studies (e.g., Zhou et al., 2023) have shown the potential of deep learning to mitigate atmospheric and topographic disturbances in InSAR time series. In this work, we propose an innovative workflow that combines optical–SAR data fusion, a hybrid U-Net/Transformer architecture, and full GIS integration, aiming to generate enhanced coherence maps and extract reliable deformation time series.

The processing chain includes precise geometric co-registration, extraction of optical features (NDVI, NDBI, texture indices), multimodal patch construction (SAR amplitude + optical bands + indices + raw coherence), and a dual-branch deep model that predicts coherence through nonlinear inter-modal correlations, inspired by the DeepInSAR framework.

Furthermore, self-supervised learning and Physics-Informed Neural Networks (PINNs) are incorporated to reduce dependency on labeled data and to enforce physical consistency (phase periodicity, signal energy conservation). These mechanisms improve model generalization across different sites and extreme events, in line with recent developments in physics-informed geohazard modeling (Moeineddin et al., 2023).

The predicted coherence maps act as quality indicators for guiding phase unwrapping and DInSAR inversion, significantly improving deformation accuracy and reliability. Final products are integrated within a GIS environment (GeoPackage / WFS / WMS) for multi-layer spatial analysis (enhanced coherence, displacement, DEM, land cover), vulnerability zoning, and decision support in land and infrastructure monitoring. This unified approach, supported by recent advances in multimodal data fusion and AI-based geospatial analytics (Sensors, 2025), illustrates the convergence of Earth observation, deep learning, and geoinformatics for more accurate and operational deformation mapping.

Keywords: DInSAR, InSAR coherence, Optical–SAR fusion, U-Net, Transformer, Self-supervised learning, PINN, GIS, Deformation mapping.

Biography:

Omar Hadj-Sahraoui is a senior researcher at the Remote Sensing Department of the Algerian Space Agency (ASAL). He holds an Engineering degree and a Magister in Computer Science & Remote Sensing, as well as a PhD with honors from the University of Science and Technology of Oran. He began his career in 1998 as a university lecturer before joining ASAL in 2000, where he has contributed to several national and international research programs. His work includes major projects on solar radiation mapping, natural disaster monitoring, and advanced InSAR processing, with strong expertise in optical–SAR image analysis, DInSAR, coherence enhancement, radar image correction, and GIS tool development. His research interests span optical and SAR image processing, geospatial data fusion, time-series analysis, and geophysical parameter retrieval. He has extensive experience with multispectral, hyperspectral, and radar satellite missions, developing methods for calibration, classification, automation, up/downscaling, and multi-sensor integration. Dr. Hadj-Sahraoui has served on the technical and scientific committees of numerous international conferences and journals, including WSEAS, ICGDA, ICAMCS, ICSSE, and several IEEE and IET publications. He has authored multiple peer-reviewed papers and conference contributions in remote sensing, image processing, and GIS applications for environmental and risk-management studies.