Affiliation:
1. National Council for Scientific Research, National Center for Remote Sensing, P.O. Box 11-8281, Beirut 11072260, Lebanon
Abstract
Urban planning depends strongly on information extracted from high-resolution satellite images such as buildings and roads features. Nowadays, most of the available extraction techniques and methods are supervised, and they require intensive labor work to clean irrelevant features and to correct shapes and boundaries. In this paper, a new model is implemented to overcome the limitations and to correct the problems of the known and conventional techniques of urban feature extraction specifically road network. The major steps in the model are the enhancement of the image, the segmentation of the enhanced image, the application of the morphological operators, and finally the extraction of the road network. The new model is more accurate position wise and requires less effort and time compared to the traditional supervised and semi-supervised urban extraction methods such as simple edge detection techniques or manual digitization. Experiments conducted on high-resolution satellite images prove the high accuracy and the efficiency of the new model. The positional accuracy of the extracted road features compared to the manual digitized ones, the counted number of detected road segments, and the percentage of completely closed and partially closed curves prove the efficiency and accuracy of the new model.
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Hardware and Architecture,Mechanical Engineering,General Chemical Engineering,Civil and Structural Engineering
Cited by
14 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献