GAN-Based Generation of Pre Disaster SAR for Earthquake Interferometry

This study proposes an earthquake disaster detection method based on interferometric synthetic aperture radar (InSAR) using synthetic pre‑disaster SAR data generated from optical satellite images. Conventional InSAR analysis requires pre‑ and post‑disaster SAR image pairs acquired under strict orbital and observation constraints, which makes it difficult to obtain suitable pre‑disaster data. In the proposed approach, a digital elevation model (DEM) and land‑cover information are combined with optical imagery, and generative adversarial networks (GANs), specifically pix2pixHD and CycleGAN, are used to generate pseudo‑SAR data that include both amplitude and phase components. Experimental results using Sentinel‑1 SAR and Sentinel‑2 multispectral instrument (MSI) data demonstrate that pix2pixHD achieves higher conversion accuracy than CycleGAN, with a peak signal‑to‑noise ratio (PSNR) of 21.25 dB and a histogram intersection of 65.25%, and that the generated pre‑disaster SAR images can be interfered with post‑disaster SAR observations to detect earthquake‑induced surface changes in the 2024 Noto Peninsula event. These findings indicate that the proposed method can extend the applicability of InSAR to areas and events where suitable pre‑disaster SAR acquisitions are unavailable, contributing to rapid earthquake disaster assessment.

@article{Arai2026,
  title     = {GAN-Based Generation of Pre Disaster SAR for Earthquake Interferometry},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Kengo Ohiwane and Hiroshi Okumura},
  doi       = {10.14569/IJACSA.2026.0170116},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170116}
}