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  A Survey on Road Extraction from Satellite Images  
  Authors : Reshma Suresh Babu; Radhakrishnan B
  Cite as:


Road extraction is the process of extracting roads from high resolution imagery and the process of extraction gives more accurate and reliable information for geographic information systems. Mainly the high resolution images are of two types: Multispectral image and Panchromatic image. Multispectral images are the one that captures image data at specific frequencies or wavelength interval. Each individual image usually has the same physical area but have different spectral band and the wavelengths are separated using filters. While panchromatic image is a single band image generally displayed as shades of gray and sensitive to all visible colors. For efficient identification, high spatial and spectral information in a single image are needed. Hence fusion of multispectral and panchromatic images is needed to convey more information. This paper presents a survey of various road extraction techniques which are used to extract roads from high resolution images.


Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 127-131

Figures :07

Tables : 01

Publication Link : A Survey on Road Extraction from Satellite Images




Reshma Suresh Babu : received her B.Tech (Computer Science & Engineering) from University of Kerala in 2014. She is currently pursuing her Masters in Computer Science & Engineering from University of Kerala. Her research interests include image processing and neural networks.

Radhakrishnan B : is working as Asst. Professor in Computer Science department. He has more than 14 years experience in teaching and has published papers on data mining and image processing. His research interests include image processing, data mining, and image mining.








Road Extraction

Multispectral Image

Panchromatic Image


Road extraction from satellite images is a challenging area due to its complexity. Road extraction identifies the road pixels from high resolution images and can be used in a variety of applications such as map implementation, traffic management, vehicle navigation, crop estimation etc. From the comparison of object-oriented and pixelbased classification methods, the object-oriented classification provides a better segmentation for high resolution satellite imagery. The meaningful segments, called objects in object-oriented segmentation provide semantic information which is necessary to interpret an image.










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