THE OPTIMAZATION OF MULTI RESOLUTION SEGMENTATION OF REMOTELY SENSED DATA USING GENETIC ALGHORITHM

Abstract

Object oriented analysis is widely used in interpretation of remote sensing images in comparison with pixel based approaches. A key step for achieving an acceptable classification result is meaningful image segmentation. Multi resolution segmentation is known as one of the most popular approaches in image segmentation that have been implemented in commercial software on the market, eCognition. However, this algorithm needs a set of optimum parameters which usually obtained by trial and error task. This technique not only is tedious and time consuming, also rely on the user's experience. So In this study in order to alleviate this problem, genetic algorithm is proposed to find the optimal parameters for multi resolution segmentation by focusing on road feature. This method is implemented on a pan-sharpened IKONOS image covering a part of Shiraz city, Iran. The results show that, with parameters found by GA, multi resolution segmentation accuracy is higher than obtained accuracy with parameters found by user. The evaluation of results confirms the importance of genetic algorithm to get optimal parameters.