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ABSTRACT
We sketch Friedrich Ackermann’s research program following the concept of Imre Lakatos, with some historical key developments in the theory and application of aerotriangulation and image matching. The research program, with its core being statistical estimation theory, has decisively influenced photogrammetry since the 60s, is still fully alive, and a challenge for today’s methods of image interpretation. We describe (1) Lakatos’ concept of a scientific research program, with its negative and positive heuristics and (2) Ackermann’s research program, clearly made explicit in his PhD, with its mathematical model, the ability to predict theoretical precision and reliability, the potential of analyzing rigorous and approximate method, and the role of testing. The development of aerotriangulation, later augmented by image matching techniques, is closely connected to Ackermann’s successful attempts to integrate basic research and practical applications.
Notes
1. This refers to the expected precision of the estimated quantities derived from the Cramer-Rao bound, namely the inverse of the normal equation matrix.
2. This is closely related to sparse image sequences.
3. The term triangulation is borrowed from geodetic networks.
4. This is the geodesy internal naming of statistical estimation theory.
5. The term “block” is taken from the French “en bloc”, referring to the simultaneous determination of all unknown entities.
6. Photogrammetry in the wide sense aims at deriving information from images. Both focus on applications in surveying, mapping and high-precision metrology, Ikeuchi (Citation2014, Chapt. Photogrammetry).
7. These are pairs of overlapping images, which were the basis for 3D mapping, and in a first step consisted of a set of 3D points and the projection centers of the two images involved. Sometimes only the xy-coordinates of the 3D points were used after enforcing the model to be leveled.
8. Originally, following the Delft School of geodesy, the functional model was named mathematical model which had an associated stochastical model, expressing the idea that both models can be changed without changing the other. We prefer to see the two models, referring to the functions and the stochastical properties as part of a joint view, since the stochastical model explicitly refers to the parameters of the functional model.
9. These are locally best linear unbiased estimates.
10. The notion “reliability” here is coherent with Huber’s (Citation1991) notion of “diagnostics”.
11. Observe, the attribute rigorous and the attribute acceptable are independent. If a method is rigorous, this does not tell whether it is acceptable for a certain application or not. On the other side, a non-rigorous method may be acceptable.
12. The abbreviation “43” indicates, that the adjustment was performed by iterating planimetric with four parameters per unit and height blocks with three parameters per unit.
13. The title refers back to the number 12 of Ebner’s (Citation1976) additional parameters.
14. Organisation Europeenne d’Etudes Photogrammetriques Experimentales – today European Spatial Data Research (EuroSDR).
15. Weight functions actually are kernel functions.
Additional information
Notes on contributors
Wolfgang Förstner
Wolfgang Förstner, studied Geodesy at Stuttgart University where he also finished his PhD. From 1990-2012 he chaired the Department of Photogrammetry at Bonn University. His fields of interest are digital photogrammetry, statistical methods of image analysis, analysis of image sequences, semantic modelling, machine learning and geo information systems. He published more than 200 scientific papers, supervised more than 100 Bachelor and Master Theses and supervised 34 PhD Theses. He served as associated editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. He obtained the Photogrammetric (Fairchild) Award of American Society of Photogrammetry and Remote Sensing 2005, honorary doctorates from the Technical University of Graz and the Leibniz University of Hannover, 2016 the Brock Gold Medal Award of the International Society of Photogrammetry and Remote Sensing (ISPRS) and 2020 the ISPRS Karl Kraus Medal for the textbook on ‘Photogrammetric Computer Vision’.