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Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping

2019-05-17 20:58:57
Rongjun Qin

Abstract

Although nowadays advanced dense image matching (DIM) algorithms are able to produce LiDAR (Light Detection And Ranging) comparable dense point clouds from satellite stereo images, the accuracy and completeness of such point clouds heavily depend on the geometric parameters of the satellite stereo images. The intersection angle between two images are normally seen as the most important one in stereo data acquisition, as the state-of-the-art DIM algorithms work best on narrow baseline (smaller intersection angle) stereos (E.g. Semi-Global Matching regards 15-25 degrees as good intersection angle). This factor is in line with the traditional aerial photogrammetry configuration, as the intersection angle directly relates to the base-high ratio and texture distortion in the parallax direction, thus both affecting the horizontal and vertical accuracy. However, our experiments found that even with very similar (and good) intersection angles, the same DIM algorithm applied on different stereo pairs (of the same area) produced point clouds with dramatically different accuracy as compared to the ground truth LiDAR data. This raises a very practical question that is often asked by practitioners: what factors constitute a good satellite stereo pair, such that it produces accurate and optimal results for mapping purpose? In this work, we provide a comprehensive analysis on this matter by performing stereo matching over 1,000 satellite stereo pairs with different acquisition parameters including their intersection angles, off-nadir angles, sun elevation & azimuth angles, as well as time differences, thus to offer a thorough answer to this question. This work will potentially provide a valuable reference to researchers working on multi-view satellite image reconstruction, as well as industrial practitioners minimizing costs for high-quality large-scale mapping.

Abstract (translated)

虽然目前先进的稠密图像匹配(DIM)算法能够从卫星立体图像中产生与之相当的光达(光探测和测距)稠密点云,但这种点云的精度和完整性在很大程度上取决于卫星立体图像的几何参数。两幅图像之间的交角通常被视为立体数据采集中最重要的一幅,因为最先进的Dim算法在窄基线(较小的交角)立体图像(例如,半全局匹配将15-25度视为良好的交角)上效果最好。这一因素与传统的航空摄影测量配置是一致的,因为交叉角直接关系到视差方向上的基准高比和纹理失真,从而影响到水平和垂直精度。然而,我们的实验发现,即使有非常相似(和良好)的交角,同样的dim算法应用于不同的立体对(同一区域)产生的点云与地面真值激光雷达数据相比,具有显著不同的精度。这就提出了一个非常实际的问题,实践者经常会问:什么因素构成了一个好的卫星立体对,从而产生精确和最佳的测绘结果?在这项工作中,我们通过对1000多对具有不同采集参数的卫星立体声进行立体匹配,包括它们的交叉角、离最低点角、太阳仰角和方位角以及时间差,对这一问题进行了全面的分析,从而为这个问题提供了一个彻底的答案。这项工作将有可能为从事多视图卫星图像重建的研究人员以及工业从业者提供有价值的参考,以最大限度地降低高质量大规模绘图的成本。

URL

https://arxiv.org/abs/1905.07476

PDF

https://arxiv.org/pdf/1905.07476.pdf


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