SGM-based seamline determination for urban orthophoto mosaicking

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• 作者：Shiyan Pang^a ; ^b ; Mingwei Sun^a ; ^{Mingweis@whu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Xiangyun Hu^a ; ^b ; Zuxun Zhang^a
• 关键词：Orthophoto mosaicking ; Seamline determination ; SGM ; Obstacle area detection ; Orthophoto&rsquo ; s approximate epipolar geometric theory
• 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：112
• 期：Complete
• 页码：1-12
• 全文大小：7168 K

文摘

Mosaicking is a key step in the production of digital orthophoto maps (DOMs), especially for large-scale urban orthophotos. During this step, manual intervention is commonly involved to avoid the case where the seamline crosses obvious objects (e.g., buildings), which causes geometric discontinuities on the DOMs. How to guide the seamline to avoid crossing obvious objects has become a popular topic in the field of photogrammetry and remote sensing. Thus, a new semi-global matching (SGM)-based method to guide seamline determination is proposed for urban orthophoto mosaicking in this study, which can greatly eliminate geometric discontinuities. The approximate epipolar geometry of the orthophoto pairs is first derived and proven, and the approximate epipolar image pair is then generated by rotating the two orthorectified images according to the parallax direction. A SGM algorithm is applied to their overlaps to obtain the corresponding pixel-wise disparity. According to a predefined disparity threshold, the overlap area is identified as the obstacle and non-obstacle areas. For the non-obstacle regions, Hilditch thinning algorithm is used to obtain the skeleton line, followed by Dijkstra’s algorithm to search for the optimal path on the skeleton network as the seamline between two orthophotos. A whole seamline network is constructed based on the strip information recorded in flight. In the experimental section, the approximate epipolar geometric theory of the orthophoto is first analyzed and verified, and the effectiveness of the proposed method is then validated by comparing its results with the results of the geometry-based, OrthoVista, and orthoimage elevation synchronous model (OESM)-based methods.