سال انتشار: ۱۳۸۶

محل انتشار: اولین کنگره مشترک سیستم های فازی و سیستم های هوشمند

تعداد صفحات: ۶

نویسنده(ها):

M Mokhtarzade – PhD Student, K.N Toosi University of Technology, Geodesy and Geomatics Faculty
H Ebadi – Assistant Professor, K.N Toosi University of Technology, Geodesy and Geomatics Faculty
M.J. Valadan Zoej – Associate Professor, K.N Toosi University of Technology, Geodesy and Geomatics Faculty

چکیده:

In this paper, neural networks are applied on high resolution IKONOS images for road detection. It was tried to optimize neural network’s functionality using a variety of texture parameters with different window sizes and gray level numbers. Both the source image and pre-classified image were used for texture parameter extraction. The obtained results were compared in terms of road and background detection accuracy. It was concluded that using texture parameters from the source image could improve road detection ability of the neural networks, while using the results of texture analysis of the pre-classified image develops the background detection accuracy.