Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Road extraction from satellite images by self-supervised classification and perceptual grouping
Date
2013-01-01
Author
Sahin, E.
Ulusoy, İlkay
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
167
views
0
downloads
Cite This
A fully automatized method which can extract road networks by using the spectral and structural features of the roads is proposed. First, Anti-parallel Centerline Extraction (ACE) is used to obtain road seed points. Then, the road seeds are improved with perceptual grouping method and the road class is determined with Maximum Likelihood Estimation (MLE) by modeling the seed points with Gaussian Mixture. The morphological operations (opening, closing and thinning) are performed for improving classification results and determining the road topology roughly. Finally, perceptual grouping is performed for removing non-road line segments and filling the gaps on the topology. The proposed algorithm is tested on 1 meter resolution IKONOS images and results better than previous algorithms are obtained
Subject Keywords
Anti-parallel Centerline
,
Gaussian Mixture
,
Perceptual grouping
,
Classification
URI
https://hdl.handle.net/11511/52181
DOI
https://doi.org/10.1117/12.2028672
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Road extraction from high-resolution satellite images
Özkaya, Meral; Temizel, Alptekin; Department of Information Systems (2009)
Roads are significant objects of an infrastructure and the extraction of roads from aerial and satellite images are important for different applications such as automated map generation and change detection. Roads are also important to detect other structures such as buildings and urban areas. In this thesis, the road extraction approach is based on Active Contour Models for 1- meter resolution gray level images. Active Contour Models contains Snake Approach. During applications, the road structure was sepa...
Monorail : an alternative transportation mode for METU
Gökbulut, Alev; Savaş Sargın, Ayşen; Department of Architecture (2003)
The aim of this thesis is to investigate an alternative mode of transportation for METU and the impacts of spatial transformations generated by the proposed system in an architectural context. This study embraces modern concepts of space-time in the practice of architectural design, and involves a sensitive consideration of the perception of space relative to position, speed and movement. In an urban context, the thesis unfolds spatial transformations affected by new movement technology. While the notions o...
Road extraction from high resolution satellite images using adaptive boosting with multi-resolution analysis
Çınar, Umut; Çetin, Yasemin; Department of Information Systems (2012)
Road extraction from satellite or aerial imagery is a popular topic in remote sensing, and there are many road extraction algorithms suggested by various researches. However, the need of reliable remotely sensed road information still persists as there is no sufficiently robust road extraction algorithm yet. In this study, we explore the road extraction problem taking advantage of the multi-resolution analysis and adaptive boosting based classifiers. That is, we propose a new road extraction algorithm explo...
Distance-based discretization of parametric signal manifolds
Vural, Elif (2010-06-28)
The characterization of signals and images in manifolds often lead to efficient dimensionality reduction algorithms based on manifold distance computation for analysis or classification tasks. We propose in this paper a method for the discretization of signal manifolds given in a parametric form. We present an iterative algorithm for the selection of samples on the manifold that permits to minimize the average error in the manifold distance computation. Experimental results with image appearance manifolds d...
Parallel implementation of a gas-kinetic BGK method on unstructured grids for 3-D inviscid missile flows
Ilgaz, Murat; Tuncer, İsmail Hakkı (2009-10-12)
A 3-D gas-kinetic BGK method and its parallel solution algorithm are developed for the computation of inviscid missile flows on unstructured grids. Flow solutions over a supersonic missile are presented to validate the accuracy and robustness of the method. It is shown that the computation time, which is an important deficiency of gas-kinetic BGK methods, may significantly be reduced by performing computations in parallel. © 2009 Springer-Verlag Berlin Heidelberg.
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
E. Sahin and İ. Ulusoy, “Road extraction from satellite images by self-supervised classification and perceptual grouping,” 2013, vol. 8892, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52181.