[论文解读] Multi-View Large-Scale Bundle Adjustment Method for High-Resolution Satellite Images

Xu Huang, Rongjun Qin|arXiv (Cornell University)|May 22, 2019

Satellite Image Processing and Photogrammetry参考文献 15被引用 6

一句话总结

本文提出了一种用于高分辨率卫星影像的多视角大规模捆绑调整方法，通过基于平面矫正和对极约束的同名点匹配算法，解决了跨多样化卫星数据集的几何与辐射畸变问题。该方法可在极低内存消耗下，高效实现数万至数百万变量的亚像素精度优化，使大规模地理空间制图在计算资源受限的条件下成为可能。

ABSTRACT

Given enough multi-view image corresponding points (also called tie points) and ground control points (GCP), bundle adjustment for high-resolution satellite images is used to refine the orientations or most often used geometric parameters Rational Polynomial Coefficients (RPC) of each satellite image in a unified geodetic framework, which is very critical in many photogrammetry and computer vision applications. However, the growing number of high resolution spaceborne optical sensors has brought two challenges to the bundle adjustment: 1) images come from different satellite cameras may have different imaging dates, viewing angles, resolutions, etc., thus resulting in geometric and radiometric distortions in the bundle adjustment; 2) The large-scale mapping area always corresponds to vast number of bundle adjustment corrections (including RPC bias and object space point coordinates). Due to the limitation of computer memory, it is hard to refine all corrections at the same time. Hence, how to efficiently realize the bundle adjustment in large-scale regions is very important. This paper particularly addresses the multi-view large-scale bundle adjustment problem by two steps: 1) to get robust tie points among different satellite images, we design a multi-view, multi-source tie point matching algorithm based on plane rectification and epipolar constraints, which is able to compensate geometric and local nonlinear radiometric distortions among satellite datasets, and 2) to solve dozens of thousands or even millions of variables bundle adjustment corrections in the large scale bundle adjustment, we use an efficient solution with only a little computer memory. Experiments on in-track and off-track satellite datasets show that the proposed method is capable of computing sub-pixel accuracy bundle adjustment results.

研究动机与目标

• 解决在捆绑调整过程中，多卫星、多时相、多分辨率高分辨率光学影像所面临的几何与辐射畸变挑战。
• 实现大规模区域的高效捆绑调整，处理数万至数百万变量，克服传统方法的内存限制。
• 开发一种鲁棒且可扩展的同名点匹配算法，适用于多种卫星传感器和成像条件。
• 在大规模数据量和传感器异质性条件下，实现捆绑调整结果的亚像素精度。

提出的方法

• 设计了一种基于平面矫正和对极约束的多视角、多源同名点匹配算法，以提升在不同卫星影像间匹配的鲁棒性。
• 通过影像矫正和基于约束的匹配，补偿卫星数据集之间的几何畸变和局部非线性辐射变化。
• 采用内存高效的解决方案，通过迭代处理变量子集而非一次性存储全部变量，求解大规模捆绑调整问题。
• 采用有理多项式系数（RPC）作为主要几何参数模型，实现统一的大地测量框架优化。
• 算法针对分布式或顺序处理进行优化，以减少内存占用，同时保持收敛性。
• 在前后视及非前后视卫星影像数据集上验证了该方法的可扩展性与精度。

实验结果

研究问题

• RQ1 如何在具有不同几何与辐射畸变的多源、多时相、多分辨率卫星影像中，可靠地提取鲁棒的同名点？
• RQ2 何种计算策略可在最小化内存使用的同时，实现包含数百万变量的大规模捆绑调整？
• RQ3 所提出方法在高分辨率卫星影像配准中，能在多大程度上实现亚像素精度？
• RQ4 平面矫正与对极约束的集成在异构卫星数据集中如何提升匹配性能？

主要发现

• 所提出的多视角同名点匹配算法能够成功识别在显著几何与辐射畸变下不同卫星影像间的鲁棒对应关系。
• 该方法在捆绑调整结果中实现了亚像素精度，经前后视与非前后视卫星数据集验证。
• 内存高效的解决方案使数万至数百万变量的捆绑调整成为可能，克服了传统存储限制。
• 该方法在大规模制图区域中表现出鲁棒性与可扩展性，即使在传感器与成像条件多样性下仍保持高精度。