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
Junction extraction on road masks by pruned skeletons
Date
2012-09-26
Author
Cinar, UMUT
KARAMAN, ERSİN
GEDİK, Ekin
ÇETİN, YASEMİN
Halıcı, Uğur
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
107
views
0
downloads
Cite This
This study proposes a new method to detect road junctions from existing road masks obtained from geospatial databases. Moreover, this method can be used to extract junction points from the road masks generated by automatic or semiautomatic road extraction algorithms. The algorithm is intended to lower the false detection rate by refining the input road mask. Vector space analysis of the pruned road skeleton provides a simple yet robust detection and classification strategy. Empirical results demonstrate the success of the proposed junction extraction model.
Subject Keywords
Junction extraction
,
Remote sensing
,
Road extraction
,
Junction type classification
URI
https://hdl.handle.net/11511/41467
DOI
https://doi.org/10.1117/12.974336
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Building Detection in satellite images by textural features and Adaboost
CETIN, MELIH; Halıcı, Uğur (2010-08-24)
A method based on textural features and Adaboost for detecting buildings in satellite images is proposed. Several local textural features including mean and standard deviation of image intensity and gradient, Zernike moments, Circular-Mellin features, Haralick features, Fourier Power Spectrum, Wavelets, Gabor Filters, and a set features extracted from HSV color space are extracted. Adaboost learning algorithm is employed for both classification and determining the beneficial feature subset, due to its featu...
Tubularity Tracking Based Automatic Road Detection from Sattelite Images
Gürbüz, Yeti Ziya; Alatan, Abdullah Aydın (2014-04-25)
In this paper, a novel approach based on tubularity tracking and graph cuts for road detection from satellites images is presented. The most important feature of the proposed method is its local peak detection filter. Unlike the tubularity based road or road like curvilinear structure detection methods presented in the literature, proposed method samples local peaks from tubularity image by tracking the peak points based on Bayesian filtering in order to construct graphs and introduces no significant comput...
Road network extraction from high-resolution multi spectral satellite images
Karaman, Ersin; Çetin, Yasemin; Department of Information Systems (2012)
In this thesis, an automatic road extraction algorithm for multi-spectral images is developed. The developed model extracts elongated structures from images by using edge detection, segmentation and clustering techniques. The study also extracts non-road regions like vegetative fields, bare soils and water bodies to obtain more accurate road map. The model is constructed in a modular approach that aims to extract roads with different characteristics. Each module output is combined to create a road score map...
A new algorithm for automatic road network extraction in multispectral satellite images
Karaman, Ersin; Çınar, Umut; Gedik, Ekin; Çetin, Yasemin; Halıcı, Uğur (2012-05-09)
The aim of this study is to develop automatic road extraction algorithm in satellite images. As roads have different width and surface material characteristics in urban and rural areas, a modular approach for road extraction algorithm is desired. In this study, edge detection, segmentation, clustering and vegetation and land cover analyses are used. In order to combine the results of different methods, a score map based on segmentation analysis is constructed. Quantitative and visual results show that this ...
Fourier Based Feature Descriptors for Railroad Extraction from Aerial Images
KARAMAN, ERSİN; Cinar, UMUT; GEDİK, Ekin; ÇETİN, YASEMİN; Halıcı, Uğur (2012-12-27)
The aim of this study is to automatically differentiate railroads from roads obtained by a general road extraction algorithm. In high resolution aerial images, the sleepers between railroad tracks depict a distinctive texture from other elongated objects. By employing this additional cue, a new Fourier based feature descriptor is developed to distinguish railroad objects in aerial images. After local Fourier features calculation, the periodic railroad texture is locally detected in the frequency domain.
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
U. Cinar, E. KARAMAN, E. GEDİK, Y. ÇETİN, and U. Halıcı, “Junction extraction on road masks by pruned skeletons,” 2012, vol. 8537, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41467.