Historical imagery is characterized by high spatial resolution and stereo-scopic acquisitions, providing a valuable resource for recovering 3D land-cover information. Accurate geo-referencing of diachronic historical images by means of self-calibration remains a bottleneck because of the difficulty to find sufficient amount of feature correspondences under evolving landscapes. In this research, we present a fully automatic approach to detecting feature correspondences between historical images taken at different times (i.e., inter-epoch), without auxiliary data required. Based on relative orientations computed within the same epoch (i.e., intra-epoch), we obtain DSMs (Digital Surface Model) and incorporate them in a rough-to-precise matching. The method consists of: (1) an inter-epoch DSMs matching to roughly co-register the orientations and DSMs (i.e, the 3D Helmert transformation), followed by (2) a precise inter-epoch feature matching using the original RGB images. The innate ambiguity of the latter is largely alleviated by narrowing down the search space using the co-registered data. With the inter-epoch features, we refine the image orientations and quantitatively evaluate the results (1) with DoD (Difference of DSMs), (2) with ground check points, and (3) by quantifying ground displacement due to an earthquake. We demonstrate that our method: (1) can automatically georeference diachronic historical images; (2) can effectively mitigate systematic errors induced by poorly estimated camera parameters; (3) is robust to drastic scene changes. Compared to the state-of-the-art, our method improves the image georeferencing accuracy by a factor of 2. The proposed methods are implemented in MicMac, a free, open-source photogrammetric software.

翻译：历史图像的特点是高空间分辨率和立体光谱获取,为恢复3D土地覆盖信息提供了宝贵的资源。通过自我校正的方式精确地对地光历史图像进行地理参照,由于难以在不断变化的地貌下找到足够数量的地貌对应信息,因此仍然是一个瓶颈。在这项研究中,我们展示了一种完全自动的方法来探测在不同时间(即跨时代)拍摄的历史图像之间的对应信息,而没有所需的辅助数据。基于在同一时代(即,内部)内计算的相对方向,我们获得了DSM(数字地平面模型)的相对方向。我们获得了DSM(数字地平面图模型)的精确地理参照,并把它们纳入粗到粗到精确的匹配中。方法包括:(1) 一个与方向大致相匹配的SMSM(即3DHelmert转换),随后又提出了一种精确的地平面特征,然后用原始的 RGB 数据精确的精确度匹配。后者的含混混不清性,通过缩小搜索空间的深度,使用共同地平面图像进行精确度分析,通过我们使用的地平面平面图像进行精确的校验算结果。