期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：23
出版社：Journal of Theoretical and Applied
摘要：The significance of extracting roads from a satellite image of high resolution can help in road navigation, update geographic information systems, emergency rescue system that requires instantaneous maps. The difficulty lies in identifying and differentiating a road from its background. To overcome this difficulty more and more research is being conducted for devising efficient methods that can extract a road from a satellite image. The large presence of buildings and manmade structures along the roads or the presence of trees or the acquisition properties of the sensor can cause difficulties in identifying a road from a satellite image. The conventional way of identifying a road was using semi-automated approaches which is not feasible as well as consumed large amounts of time. A high accuracy technique for road extraction is needed. Also when compared with extraction from aerial images the extraction from satellite images is a challenging domain. The reason for that is satellite images have a resolution that is low and the presence of noise contents in any given image is quite high. This work deals with extracting a road network from high resolution satellite images. This work deals with estimating if a pixel in a satellite image is part of a road or not using Convolutional Neural Network. The advent of Tensor flow library has made this work feasible. The work proposes a new approach for making data sets for this complex problem and has concluded with a feasible solution for the problem.
