Stereo matching of high-resolution satellite images (HRSI) is still a
fundamental but challenging task in the field of photogrammetry and remote
sensing. Recently, deep learning (DL) methods, especially convolutional neural
networks (CNNs), have demonstrated tremendous potential for stereo matching on
public benchmark datasets. However, datasets for stereo matching of satellite
images are scarce. To facilitate further research, this paper creates and
publishes a challenging dataset, termed WHU-Stereo, for stereo matching DL
network training and testing. This dataset is created by using airborne LiDAR
point clouds and high-resolution stereo imageries taken from the Chinese
GaoFen-7 satellite (GF-7). The WHU-Stereo dataset contains more than 1700
epipolar rectified image pairs, which cover six areas in China and includes
various kinds of landscapes. We have assessed the accuracy of ground-truth
disparity maps, and it is proved that our dataset achieves comparable precision
compared with existing state-of-the-art stereo matching datasets. To verify its
feasibility, in experiments, the hand-crafted SGM stereo matching algorithm and
recent deep learning networks have been tested on the WHU-Stereo dataset.
Experimental results show that deep learning networks can be well trained and
achieves higher performance than hand-crafted SGM algorithm, and the dataset
has great potential in remote sensing application. The WHU-Stereo dataset can
serve as a challenging benchmark for stereo matching of high-resolution
satellite images, and performance evaluation of deep learning models. Our
dataset is available at this https URL

本研究创建了一份包含超过 1700 个成对高分辨率遥感卫星图像的 WHU-Stereo 数据集，以促进深度学习网络的立体匹配训练和测试，并探究其在遥感应用中的潜力。实验证明，该数据集的性能可与现有的立体匹配数据集可比，并且深度学习模型的性能要比手工设计的 SGM 算法更高。

WHU-Stereo：高分辨率卫星图像立体匹配的挑战基准

WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images

Analysis of high-resolution satellite images has been an important research
topic for traffic management, city planning, and road monitoring. One of the
problems here is automatic and precise road extraction. From an original image,
it is difficult and computationally expensive to extract roads due to presences
of other road-like features with straight edges. In this paper, we propose an
approach for automatic road extraction based on a fully convolutional neural
network of U-net family. This network consists of ResNet-34 pre-trained on
ImageNet and decoder adapted from vanilla U-Net. Based on validation results,
leaderboard and our own experience this network shows superior results for the
DEEPGLOBE - CVPR 2018 road extraction sub-challenge. Moreover, this network
uses moderate memory that allows using just one GTX 1080 or 1080ti video cards
to perform whole training and makes pretty fast predictions.

本文提出了一种基于全卷积神经网络的自动道路提取方法，利用高分辨率的卫星图像进行交通管理和道路监控，此方法能够自动且精确地提取真正的道路信息，并在 DEEPGLOBE-CVPR 2018 道路提取子挑战中表现出卓越的效果。