The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLI-B7
21 Jun 2016
 | 21 Jun 2016


C. Colomo-Jiménez, J. L. Pérez-García, T. Fernández-del Castillo, J. M. Gómez-López, and A. T. Mozas-Calvache

Keywords: Photogrammetry, LiDAR, data fusion, multitemporal analyses

Abstract. Nowadays, data fusion is one of the trends in geomatics sciences, due to the necessity of merging data from different kind of sensors and periods of time. Also, to extrract the maximum information from data and useful multitemporal analysis, an exact geoconnection of all datasets in a common and stable reference system is essential. The results of the application of a methodology for an integrated orientation into a common reference system using data obtained by LiDAR systems, digital and historical photogrammetric flights dataset, used for proper analysis in multitemporal studies, are presented in this paper. In order to analyse the results of the presented methodology, several photogrammetric datasets have been used. This data corresponds with digital and analogic data. The most current flight (2010) combines data obtained with digital photogrammetric camera and LiDAR sensor which will be used as reference model for all subsequent photogrammetry flights. The philosophy of the methodology consists of orientating all photogrammetric flights to the DEM obtained by LiDAR data. All the models obtained from every photogrammetric block are comparable in terms of the geometric resolution of each one. For that reason, altimetric stable points are extracted automatically from the LiDAR points cloud to use these points such as altimetric control point in the different flights that must be oriented. Using LiDAR control points, we demonstrate the improvement in the results between initial orientation and final results. Also it is possible to improve the planimetric correspondence between different photogrammetric blocks using only altimetric control points iteratively.