ETRI JOURNAL, cilt.27, ss.294-303, 2005 (SCI İndekslerine Giren Dergi)
In this paper, a supervised algorithm for the evaluation of geophysical sites using Multi Level- Cellular Neural Network (ML-CNN) is introduced, developed and then applied to real data. ML-CNN is a stochastic image processing technique which is based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. Genetic algorithm is used in optimization of CNN templates. The first application is concerned with the separation of potential field data of Dumluca chromite region which is one of the rich reserves of Turkey; in this context the classical approach to gravity anomaly separation method, is one of the main problems in geophysics. The other example is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at in Sivas-Altinyayla region of Turkey, are among the most important archeological sites in history; one reason among others being that written documentation was first produced by this civilization.