10 April 2018 Automatic digital surface model (DSM) generation from aerial imagery data
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106153K (2018) https://doi.org/10.1117/12.2303406
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Aerial sensors are widely used to acquire imagery for photogrammetric and remote sensing application. In general, the images have large overlapped region, which provide a lot of redundant geometry and radiation information for matching. This paper presents a POS supported dense matching procedure for automatic DSM generation from aerial imagery data. The method uses a coarse-to-fine hierarchical strategy with an effective combination of several image matching algorithms: image radiation pre-processing, image pyramid generation, feature point extraction and grid point generation, multi-image geometrically constraint cross-correlation (MIG3C), global relaxation optimization, multi-image geometrically constrained least squares matching (MIGCLSM), TIN generation and point cloud filtering. The image radiation pre-processing is used in order to reduce the effects of the inherent radiometric problems and optimize the images. The presented approach essentially consists of 3 components: feature point extraction and matching procedure, grid point matching procedure and relational matching procedure. The MIGCLSM method is used to achieve potentially sub-pixel accuracy matches and identify some inaccurate and possibly false matches. The feasibility of the method has been tested on different aerial scale images with different landcover types. The accuracy evaluation is based on the comparison between the automatic extracted DSMs derived from the precise exterior orientation parameters (EOPs) and the POS.
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Nan Zhou, Nan Zhou, Shixiang Cao, Shixiang Cao, Hongyan He, Hongyan He, Kun Xing, Kun Xing, Chunyu Yue, Chunyu Yue, } "Automatic digital surface model (DSM) generation from aerial imagery data", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106153K (10 April 2018); doi: 10.1117/12.2303406; https://doi.org/10.1117/12.2303406

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