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NEURAL NETWORKS IN THE AUTOMATION OF PHOTOGRAMMETRIC 
PROCESSES 
S. Mikrut 3 '*, Z. Mikrut b 
a AGH, Dept, of Geoinformation, Photogrammetry and Environmental Remote Sensing, University of Science and 
Technology Cracow (AGH), 30059 Krakow, Poland - smikrut@agh.edu.pl 
b AGH, Dept, of Automatics, University of Science and Technology Cracow (AGH), 30059 Krakow, 
Poland - zibi@agh.edu.pl 
Commission IV, WG IV/3 
KEY WORDS: Photogrammetry, Automation, Correlation, Extraction, Recognition, Neural 
ABSTRACT: 
The concept of research was based on the selection of several representations, which later were correlated by means of classic, and 
neural methods. In the course of research, classic methods of image matching were tested and compared with neural methods that 
originated in the course of research. Additionally, experiments consisting in manual measurements, performed by independent 
observers, were conducted. The essence of methodology that was based on neural networks consisted in the preparation of suitable 
representations of image fragments and using them for the classification of various types of neural networks. One of the assumed 
methods was based on the distribution of image gradient module value and of its direction. The usability of that representation for 
the selection of sub-images was tested by means of SOM Kohonen neural network. Another method consisted in the utilization of 
the log-polar and log-Hough transforms, which are considered to be simplified models of preliminary image processing, performed 
by visual systems of people and animals. The usability of that representation was tested by means of the backpropagation type of 
neural network. As regards the generation of the third representation, the ICM (Intersecting Cortical Model) network was applied, 
which is one of the versions of the PCNN (Pulse Coupled Neural Network). Using that network, the so-called image signatures, or 
vectors composed of tens of elements which describe the image structure, were generated. 
1. INTRODUCTION 
The use of neural networks to solve photogrammetric problems 
is a relatively new direction of research, although those 
networks have been used in digital image analysis for long. The 
problem consists not only in the lack of popularization of that 
technique among photogrammetry specialists. An additional 
difficulty results from the specificity of photogrammetric 
images, which are marked by a high diversity and 
fragmentation of information. The smaller is the scale of images, 
the greater is that fragmentation. This gives origin to troubles 
with the creation of a set of data for teaching and testing, as that 
set should, first of all, be representative. Another difficulty is 
the lack of neural tools, with which one could perform one of 
the most significant stages of the analysis, that is matching 
(correlating). Therefore, there remain issues related to the 
classification of areas and detection of features, as shown by the 
application examples drawn from literature and presented in 
Chapter 2. 
The authors of this paper have proposed a conception of using 
neural networks for the preliminary selection of sub-images and, 
indirectly, for matching them. The paper is a recapitulation of 
several years' works. The research conception was based on a 
selection of several representations, which were later used as 
inputs for the classifying neural networks. Broadly understood 
neural networks were also used to create representations, which 
were then correlated. The classic methods of image matching 
were tested and compared with neural methods, which 
originated in the course of research. Additionally, experiments 
consisting in manual measurements performed by independent 
observers were conducted. 
The arrangement of the paper is the following. Chapter 2 
presents examples of the application of neural networks in 
photogrammetry and in related issues taken from the literature 
on the subject. Chapter 3 recollects principles, associated with 
the use of neural networks technique, as well as discusses to a 
greater extent a less known ICM (Intersecting Cortical Model) 
pulse-coupled network. The main part of the paper is included 
in Chapter 4, which presents the authors' ideas related to the 
feasibility of application of neural networks on different stages 
of photogrammetric image analysis. It also discusses briefly 
those concepts, which have been implemented. Chapter 5 
includes the specification of results and conclusions. 
2. EXAMPLES OF THE APPLICATION OF NEURAL 
NETWORKS IN PHOTOGRAMMETRY AND REMOTE 
SENSING 
The attempts to use neural networks to solve various problems 
in the fields of photogrammetry and remote sensing have been 
made, practically speaking, from the very beginning of their 
development. The possibilities of using an artificial intelligence 
in photogrammetry were presented as early as in 1988 (in a 
general outline) by T. Sarjakoski (Sarjakoski, 1988). 
One of the first applications concerned the use of networks for 
feature extraction (Zheng, 1992). At the same time, Hu (during 
Corresponding author.