期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B3
页码：387-392
出版社：Copernicus Publications
摘要：In this article, the possibility of using artificial neural networks for road detection from high resolution satellite images is tested on a part of RGB Ikonos and Quick-Bird images from Kish Island and Bushehr Harbour respectively. Then, the effects of different input parameters on network's ability are verified to find out optimum input vector for this problem. A variety of network structures with different iteration times are used to determine the best network structure and termination condition in training stage. It was discovered when the input parameters are made up of spectral information and distances of pixels to road mean vector in a 3*3 window, network's ability in both road and background detection can be improved in comparison with simple networks that just use spectral information of a single pixel in their input vector
关键词：Remote Sensing; Extraction; Neural; Networks; Learning; High resolution; IKONOS; Quickbird
