alexa Abstract | Evaluation of the Full Lambda and SVM Methods Capability to Extract Roads from Digital Images
ISSN: 2469-4134

Journal of Remote Sensing & GIS
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article Open Access


Automatic extraction of information on ground using photogrammetry and remote sensing requires the formulation of human data and image data, so that, it must include all the content of the image. Complex structure of the various objects in the image leads to the challenges for doing this. So, choose the type of digital data and a good way to extract the desired effect is important in mapping accuracy. This study has investigated the semi-automated method of extraction of various types, including straight, spiral, intersection, urban and non-urban roads from satellite and aerial images. Data used included UltraCam aerial image, Worldview satellite image of non-urban area with a resolution of 0.5 m, and Quick-Bird images of Tehran province with a resolution of 0.61 m. In the proposed method, upon performing image segmentation by using Full lambda method, image classification has been done using SVM algorithm, and morphological operations are used to improve the quality of discover ways and remove noise and cover gaps. For images in which Full lambda method has high accuracy in image segmentation, therefore, the accuracy of the image classification is increased and extraction of the road from it has been done better. The average overall accuracy of over 81 percent and the average accuracy Kappa coefficient more than 78 percent in the image classification into two classes of road and non-road indicates very good capability of the system introduced for semi-automatic extraction of different roads.

To read the full article Peer-reviewed Article PDF image | Peer-reviewed Full Article image

Author(s): Abdollahi A, Bakhtiari HRR and Nejad PM


Extract road, SVM, Full lambda, Digital images, Geomatics, Geophysics, Geovisualization

Share This Page

Additional Info

Loading Please wait..
Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version