Stereo Radargrammetry Using Deep Learning from Airborne SAR Images
Journal:
arXiv
Published Date:
May 27, 2025
Abstract
In this paper, we propose a stereo radargrammetry method using deep learning
from airborne Synthetic Aperture Radar (SAR) images. Deep learning-based
methods are considered to suffer less from geometric image modulation, while
there is no public SAR image dataset used to train such methods. We create a
SAR image dataset and perform fine-tuning of a deep learning-based image
correspondence method. The proposed method suppresses the degradation of image
quality by pixel interpolation without ground projection of the SAR image and
divides the SAR image into patches for processing, which makes it possible to
apply deep learning. Through a set of experiments, we demonstrate that the
proposed method exhibits a wider range and more accurate elevation measurements
compared to conventional methods. The project web page is available at:
https://gsisaoki.github.io/IGARSS2025_sasayama/
