用深度学习提升机载SAR立体测高精度与范围。
Stereo Radargrammetry Using Deep Learning from Airborne SAR Images
- 将SAR图像分块处理，避免投影失真，适配深度学习。
- 相比传统方法，高程测量范围更广、精度更高。
- 自建SAR数据集，支持模型微调，填补公开数据空白。
本文提出一种基于深度学习的机载合成孔径雷达（SAR）立体测高方法。深度学习方法被认为对几何图像调制影响较小，但目前缺乏用于训练此类方法的公开SAR图像数据集。为此，我们构建了一个SAR图像数据集，并对基于深度学习的图像匹配方法进行了微调。所提方法通过图像分块处理，避免了像素插值带来的质量退化及地面投影，使深度学习得以有效应用。实验结果表明，该方法在高程测量范围和精度上均优于传统方法。项目网页见：https://gsisaoki.github.io/IGARSS2025_sasayama/
原文摘要 · Abstract (English)
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/
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