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基于光学遥感影像的三维场景重建是计算机视觉和遥感领域中一个重要的研究主题，其旨在通过一组多视角光学遥感影像自动恢复场景的三维结构。近年来，基于光学遥感影像的三维重建技术在城市规划、自主导航等诸多领域展现出巨大的应用潜力。本文围绕着基于光学遥感影像（尤其是卫星光学遥感影像）三维重建中存在的重建速度慢、完整度低等问题进行了探索，主要工作如下：
（1）文献中基于卫星光学遥感影像的三维重建方法大多采用基于有理多项式系数（Rational Polynomial Coefficients，RPC）模型的增量式重建策略进行场景三维建模，其重建速度和完整度普遍较低。针对该问题，本文提出了一种基于仿射成像模型的卫星遥感影像快速重建方法。首先，该方法将输入的多视角卫星影像裁剪成一组含有重叠区域的小尺寸影像块，并利用这些影像块分别计算出相应局部场景的三维仿射点云。然后，设计一种基于局部点云的全局式仿射运动矩阵估计算法计算出每个视角对应相机的仿射运动矩阵。最后，利用相机的仿射运动矩阵和少量地面控制点恢复出场景的三维欧氏结构。由于该方法不需要执行反复的捆绑调整优化，且独立于有理多项式系数模型的先验信息，因此可以实现快速的卫星遥感影像三维重建。在MVS3DM 和DFC2019 数据集上的对比结果表明，该方法的重建速度、精度和完整性在大多数情况下均优于文献中三种主流三维重建方法。
（2）为了进一步缩短光学遥感影像三维重建过程的计算时间，本文提出了一种基于场景图分割的快速三维重建方法。首先，该方法利用影像对之间的几何信息构造初始外极几何图，并通过聚类算法将其划分为多个独立的子场景图。然后，根据初始外极几何图构建的最大生成树对子场景图进行扩张，以增强子场景图之间的连接关系。进而，基于每个扩张的子场景图分别进行三维重建以获得相应局部场景的三维结构。最后，利用最大生成树的路径对局部场景结构进行合并以得到整个目标场景的三维结构。在2 个卫星光学遥感数据集与3 个无人机光学遥感数据集上的实验结果表明，与文献中四种主流方法相比，该方法能够在保持相似重建精度与完整度的前提下，实现更为快速的场景三维重建。

3D scene reconstruction from optical remote sensing images is an important research topic in computer vision and remote sensing, which aims to automatically recover the 3D scene structure from a set of multi-view remote sensing images. In recent years, 3D reconstruction from optical remote sensing images has shown great application potential in many fields, such as urban planning, autonomous navigation, etc. This
thesis investigates the problems of low speed and completeness in 3D reconstruction from optical remote sensing images, and its main works include:
(1) Most of the existing methods in literature employ the RPC (Rational Polynomial Coefficients) model for 3D reconstruction from optical remote sensing images in an incremental reconstruction manner, and their reconstruction speed and completeness are generally low. To address this problem, a fast reconstruction method for optical satellite images based on the affine imaging model is proposed. Firstly, the input multi-view satellite images are cropped into a set of small-sized patches with overlapping regions, and for each pair of patches that have a sufficient number of point correspondences fromtwo views, the corresponding 3D affine point cloud is calculated. Then based on the
obtained local point clouds, a global affine camera motion estimation algorithm is explored for calculating the affine motion matrices of the cameras corresponding to all the patches in a unified coordinate system. Finally, the obtained affine camera motion matrices and a small number of ground control points are utilized to recover the Euclidean scene structure. The proposed method, which is independent of the prior information of the RPC model, does not perform bundle adjustment repeatedly, achieving a fast reconstruction for satellite images. Experimental results on the MVS3DM and DFC2019 datasets demonstrate that the reconstruction speed, accuracy and completeness of this method are better than three state-of-the-art 3D reconstruction methods in most cases.
(2) In order to further reduce the computational cost of 3D reconstruction from optical remote sensing images, a fast 3D reconstruction method based on scene graph partition is proposed. Specifically, we first construct an initial epipolar geometry graph according to the geometric information between pairs of images, and divide it into several independent sub-scene graphs by utilizing a clustering algorithm. Then, the maximum spanning tree is constructed according to the initial epipolar geometry graph, and it is used to expand the sub-scene graphs to enhance the connections among the obtained sub-scene graphs. Next, based on each expanded sub-scene graph, the corresponding local scene is reconstructed respectively. Finally, all the reconstructed local scenes are
merged into a complete 3D scene by utilizing the maximum spanning tree. Experimental results on two satellite optical remote sensing datasets and three UAV(Unmanned Aerial Vehicle) optical remote sensing datasets demonstrate that the proposed method achieves close reconstruction accuracy and completeness, but performs faster in comparison to four state-of-the-art methods.