Image Processing on Ruins of Hitite Civilization Using Random Neural Network Approach

Abstract

Geophysical methods have been widely used in recent years to display, model and uncover buried archaeological artifacts. In line with the information received from the archaeologists, it is decided which geophysical methods should be used in the archaeological structures to be searched. Thus, as a result of the studies, we can have information about the location and location of the archaeological work. In this way, we can direct the archaeological excavation and make the excavation to the depth where the artifact can be found without damaging the archaeological artifact and determine the limits of archaeological artifacts. In this paper, Random Neural Network (RNN) has been applied to a real archaeological magnetic map belonging to ancient Hittite civilization, obtained by an international research group. RNN is a contemporary, stochastic approach for 2-D image processing for real-time models. The domain-dependent prior knowledge, such as the sizes, the shapes, the depth and the orientations of observed regions can be reflected in the parameters of RNN structure. We have separated regional and residual anomalies of Hittite civilization using RNN and extracted the historical Sarissa -Kusakli walls close to Sivas city in Turkey. We have shown that the performance of RNN is better than classical derivative based techniques.