Automation Aspects for the Georeferencing of Photogrammetric Aerial Image Archives in Forested Scenes

• Published in: Remote Sensing
• Main Authors: Nurminen, Kimmo, Litkey, Paula, Honkavaara, Eija, Vastaranta, Mikko, Holopainen, Markus, Lyytikäinen-Saarenmaa, Päivi, Kantola, Tuula, Lyytikäinen, Minna
• Other Authors: Department of Forest Sciences, Laboratory of Forest Resources Management and Geo-information Science, Forest Health Group, Forest Ecology and Management
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI 2016
• Subjects: CAMERA CALIBRATION; CADASTRAL MAPS; PHOTOGRAPHS; MODELS; LIDAR; GENERATION; 4112 Forestry; karelian
• Online Access: http://hdl.handle.net/10138/159389

Photogrammetric aerial film image archives are scanned into digital form in many countries. These data sets offer an interesting source of information for scientists from different disciplines. The objective of this investigation was to contribute to the automation of a generation of 3D environmental model time series when using small-scale airborne image archives, especially in forested scenes. Furthermore, we investigated the usability of dense digital surface models (DSMs) generated using these data sets as well as the uncertainty propagation of the DSMs. A key element in the automation is georeferencing. It is obvious that for images captured years apart, it is essential to find ground reference locations that have changed as little as possible. We studied a 68-year-long aerial image time series in a Finnish Karelian forestland. The quality of candidate ground locations was evaluated by comparing digital DSMs created from the images to an airborne laser scanning (ALS)-originated reference DSM. The quality statistics of DSMs were consistent with the expectations; the estimated median root mean squared error for height varied between 0.3 and 2 m, indicating a photogrammetric modelling error of 0.1 parts per thousand with respect to flying height for data sets collected since the 1980s, and 0.2 parts per thousand for older data sets. The results show that of the studied land cover classes, "peatland without trees" changed the least over time and is one of the most promising candidates to serve as a location for automatic ground control measurement. Our results also highlight some potential challenges in the process as well as possible solutions. Our results indicate that using modern photogrammetric techniques, it is possible to reconstruct 3D environmental model time series using photogrammetric image archives in a highly automated way. Peer reviewed