One of the main products of multi-view stereo (MVS) high-resolution satellite (HRS) images in photogrammetry and remote sensing is digital surface model (DSM). Producing DSMs from MVS HRS images still faces serious challenges due to various reasons such as complexity of imaging geometry and exterior orientation model in HRS, as well as large dimensions and various geometric and illumination variations. The main motivation for conducting this research is to provide a novel and efficient method that enhances the accuracy and completeness of extracting DSM from HRS images compared to existing recent methods. The proposed method called Sat-DSM, consists of five main stages. Initially, a very dense set of tie-points is extracted from the images using a tile-based matching method, phase congruency-based feature detectors and descriptors, and a local geometric consistency correspondence method. Then, the process of Rational Polynomial Coefficients (RPC) block adjustment is performed to compensate the RPC bias errors. After that, a dense matching process is performed to generate 3D point clouds for each pair of input HRS images using a new geometric transformation called PWB (pricewise bilinear) and an accurate area-based matching method called SWLSM (selective weighted least square matching). The key innovations of this research include the introduction of SWLSM and PWB methods for an accurate dense matching process. The PWB is a novel and simple piecewise geometric transformation model based on superpixel over-segmentation that has been proposed for accurate registration of each pair of HRS images. The SWLSM matching method is based on phase congruency measure and a selection strategy to improve the well-known LSM (least square matching) performance. After dense matching process, the final stage is spatial intersection to generate 3D point clouds, followed by elevation interpolation to produce DSM. To evaluate the Sat-DSM method, 12 sets of MVS-HRS data from IRS-P5, ZY3-1, ZY3-2, and Worldview-3 sensors were selected from areas with different landscapes such as urban, mountainous, and agricultural areas. The results indicate the superiority of the proposed Sat-DSM method over four other methods CATALYST, SGM (Semi-global matching), SS-DSM (structural similarity based DSM extraction), and Sat-MVSF in terms of completeness, RMSE, and MEE. The demo code is available at https://www.researchgate.net/publication/377721674_SatDSM.

中文翻译：

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用于精确生成卫星 DSM 的选择性加权最小二乘和分段双线性变换


数字表面模型 （DSM） 是摄影测量和遥感领域多视图立体 （MVS） 高分辨率卫星 （HRS） 图像的主要产品之一。由于 HRS 中成像几何和外部定向模型的复杂性以及大尺寸和各种几何和照明变化等各种原因，从 MVS HRS 图像生成 DSM 仍然面临严峻的挑战。进行这项研究的主要动机是提供一种新颖有效的方法，与现有的最新方法相比，该方法可以提高从 HRS 图像中提取 DSM 的准确性和完整性。提出的方法称为 Sat-DSM，由五个主要阶段组成。最初，使用基于平铺的匹配方法、基于相位同余的特征检测器和描述符以及局部几何一致性对应方法从图像中提取一组非常密集的连接点。然后，执行有理多项式系数 （RPC） 区域网平差过程以补偿 RPC 偏置误差。之后，执行密集匹配过程，使用称为 PWB（价格双线性）的新几何变换和称为 SWLSM（选择性加权最小二乘匹配）的基于区域的精确匹配方法为每对输入 HRS 图像生成 3D 点云。这项研究的主要创新包括引入 SWLSM 和 PWB 方法以实现精确的密集匹配过程。PWB 是一种基于超像素过分割的新颖而简单的分段几何转换模型，已被提出用于精确配准每对 HRS 图像。SWLSM 匹配方法基于相位一致性测量和选择策略，以提高众所周知的 LSM （最小二乘匹配） 性能。 经过密集匹配过程后，最后阶段是空间交集生成 3D 点云，然后是高程插值生成 DSM。为了评估 Sat-DSM 方法，从 IRS-P5、ZY3-1、ZY3-2 和 Worldview-3 传感器的 12 组 MVS-HRS 数据中选择了城市、山区和农业区等不同景观的地区。结果表明，所提出的 Sat-DSM 方法在完整性、RMSE 和 MEE 方面优于其他四种方法 CATALYST、SGM（半全局匹配）、SS-DSM（基于结构相似性的 DSM 提取）和 Sat-MVSF。演示代码可在 https://www.researchgate.net/publication/377721674_SatDSM 获取。