Stereo Radargrammetry Using Deep Learning from Airborne SAR Images
By: Tatsuya Sasayama , Shintaro Ito , Koichi Ito and more
Potential Business Impact:
Makes maps from space pictures more accurate.
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/
Similar Papers
Multi-view 3D surface reconstruction from SAR images by inverse rendering
CV and Pattern Recognition
Maps 3D shapes from radar pictures.
From Spaceborne to Airborne: SAR Image Synthesis Using Foundation Models for Multi-Scale Adaptation
Image and Video Processing
Makes satellite pictures look like airplane pictures.
SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling
CV and Pattern Recognition
Finds small things in satellite pictures.