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.
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
Fusion
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.
[1] Mourad Bouziani, Kalifa Goita, Dong-Chen He ,”Rule-
Based classification of a very high resolution image in
an urban enviornment using multispectral segmentation
guided by cartographic data”, IEEE
[2] transactions on geosciences and remote sensing, vol.48,
no.8, August 2010, pp.3198-3211.
[3] M.Kumar, R.K.Singh, P.L.N.Raju,
Y.V.N.Krishnamurthy ,”Road network extraction from
high resolution multispectral satellite imagery based on
object oriented techniques”, ISPRS annals of the
photogrammetry, Remote sensing and spatial
information sciences, volume II-8, 2014, pp.107-110.
[4] Neha Gupta, H.S.Baduria, “Object based information
extraction from high resolution satellite imagery using
eCognition”, IJCSI International Journal of Computer
Science Issues, Vol. 11, Issue 3, No 2, May 2014,
pp.139-144.
[5] Sun Xiaoxia, Zhang Jixian, Liu Zhengjun, “An objectoriented
classification method on high resolution
satellite data”, ACRS, 2004, pp.347-350.
[6] Xinliang Li, Shuhe Zhao, Yikang Rui, Wei Tang, “An
object-based classification approach for high-spatial
resolution imagery”, proceedings of the society of
photo-optical instrumentation engineers (SPIE), Vol.
6752.
[7] Uwe Bacher, Helmut Mayer, “Automatic road
extraction from multispectral high resolution satellite
images”, IAPRS, vol. XXXVI, part 3/W24, Austria,
August 2005, pp.29-34.
[8] Yiting Wang, Xinliang Li, Liqiang Zhang, Wuming
Zhang, “Automatic road extraction of urban area from
high spatial resolution remotely sensed imagery”, The
international archives of the photogrammetry, Remote
sensing and spatial information sciences, vol. XXXVII,
part B6b, Beijing 2008.
[9] Irene Walde, Bjorn Frohlich, Eric Bach, Soren Hese,
Christiane Schmullius, Joachim Denzler, “Land cover
classification of satellite images using contextual
information”, ISPRS Annals of the Photogrammetry,
Remote Sensing and Spatial Information Sciences,
Volume II-3/W1, 2013.
[10] Qiaoping Zhang, Isabelle Couloigner, “Automated road
network extraction from high resolution multi-spectral
imagery”, American society for photogrammetry and
remote sensing(ASPRS), May 2006.
[11] Pete Doucette, Peggy Agouris, and Anthony Stefanidis,
“Automated road extraction from high resolution
multispectral imagery”, Photogrammetric Engineering
& Remote Sensing, Vol. 70, No. 12, December 2004,
pp. 1405–1416.
[12] M.Ziems, M.Gerke, C.Heipke, “Automatic road
extraction from remote sensing imagery incorporating
prior information and color segmentation”,
International archives of photogrammetry,remote
sensing and spatial information sciences, September
2007, pp.141-147.
[13] C.Heipke, H.Mayer, C.Wiedemann, “Evaluation of
automatic road extraction”, IAPRS, vol.32, September
1997, pp.151-160.
[14] Mingjun Song, Daniel Civco, “Road extraction using
SVM and image segmentation”, Photogrammetric
engineering and remote sensing, vol.70, December
2004, pp.1365-1371.
[15] Chunsun Zhang, Emmanuel Baltsavias, “Automatic
road extraction by integrated analysis of images and
GIS data”, American society for photogrammetry and
remote sensing(ASPRS), May 2007.
[16] J.B.Mena “State of the art on automatic road extraction
for GIS update: a novel classification”, ELSEVIER,
Pattern recognition letters, 2003, pp.3037-3058.
[17] Marie Flavie, Auclair Fortier, Djemel Ziou, Costas
Armenakis, Shengrui Wang, “Automated correction
and updating of roads from aerial ortho-images”,
International archives of photogrammetry and remote
sensing, vol.XXXIII, Amsterdam, 2000.
[18] Cui Ni, Qin Ye, Bofeng Li, Shaoming Zhang, “Road
extraction from high-resolution remote sensing image
based on phase classification”, The International
Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences, Vol. XXXVII, Part B3b,
Beijing 2008.
