Neural Networks in the Automation of Photogrammetric Processes

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. * Corresponding author.