Abstract
Stereo dense image matching can be categorized to low-level feature based matching and deep feature based matching according to their matching cost metrics. Census has been proofed to be one of the most efficient low-level feature based matching methods, while fast Convolutional Neural Network (fst-CNN), as a deep feature based method, has small computing time and is robust for satellite images. Thus, a comparison between fst-CNN and census is critical for further studies in stereo dense image matching. This paper used cost function of fst-CNN and census to do stereo matching, then utilized semi-global matching method to obtain optimized disparity images. Those images are used to produce digital surface model to compare with ground truth points. It addresses that fstCNN performs better than census in the aspect of absolute matching accuracy, histogram of error distribution and matching completeness, but these two algorithms still performs in the same order of magnitude.
Abstract (translated)
立体密集图像匹配根据其匹配成本指标可分为低层次特征匹配和深层特征匹配。人口普查已被证明是最有效的低层次特征匹配方法之一，而快速卷积神经网络（FST CNN）作为一种深度特征匹配方法，计算时间短，对卫星图像具有鲁棒性。因此，比较FST CNN和人口普查对于进一步研究立体密集图像匹配至关重要。本文利用FST CNN的成本函数和人口普查进行立体匹配，然后利用半全局匹配方法获得最佳的视差图像。这些图像用于生成数字表面模型，与地面真值点进行比较。该算法在绝对匹配精度、误差分布直方图和匹配完整性等方面优于人口普查算法，但这两种算法仍然具有相同的量级。
URL
https://arxiv.org/abs/1905.09147
