World Library  


Add to Book Shelf
Flag as Inappropriate
Email this Book

Automated Extraction of Buildings and Roads in a Graph Partitioning Framework : Volume Ii-3/W3, Issue 1 (08/10/2013)

By Ok, A. O.

Click here to view

Book Id: WPLBN0004013838
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Automated Extraction of Buildings and Roads in a Graph Partitioning Framework : Volume Ii-3/W3, Issue 1 (08/10/2013)  
Author: Ok, A. O.
Volume: Vol. II-3/W3, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Ok, A. O. (2013). Automated Extraction of Buildings and Roads in a Graph Partitioning Framework : Volume Ii-3/W3, Issue 1 (08/10/2013). Retrieved from http://www.ebooklibrary.org/


Description
Description: Department of Civil Engineering, Faculty of Engineering, Mersin University, 33343, Mersin, Turkey. This paper presents an original unsupervised framework to identify regions belonging to buildings and roads from monocular very high resolution (VHR) satellite images. The proposed framework consists of three main stages. In the first stage, we extract information only related to building regions using shadow evidence and probabilistic fuzzy landscapes. Firstly, the shadow areas cast by building objects are detected and the directional spatial relationship between buildings and their shadows is modelled with the knowledge of illumination direction. Thereafter, each shadow region is handled separately and initial building regions are identified by iterative graph-cuts designed in a two-label partitioning. The second stage of the framework automatically classifies the image into four classes: building, shadow, vegetation, and others. In this step, the previously labelled building regions as well as the shadow and vegetation areas are involved in a four-label graph optimization performed in the entire image domain to achieve the unsupervised classification result. The final stage aims to extend this classification to five classes in which the class road is involved. For that purpose, we extract the regions that might belong to road segments and utilize that information in a final graph optimization. This final stage eventually characterizes the regions belonging to buildings and roads. Experiments performed on seven test images selected from GeoEye-1 VHR datasets show that the presented approach has ability to extract the regions belonging to buildings and roads in a single graph theory framework.

Summary
Automated Extraction of Buildings and Roads in a Graph Partitioning Framework

 

Click To View

Additional Books


  • Promoting the Effect of the Qing Dynasty ... (by )
  • Automatic Detection and Feature Estimati... (by )
  • Web-based Delivery System for Disaster P... (by )
  • Parameter-based Performance Analysis of ... (by )
  • A Method for Virtual Anastylosis: the Ca... (by )
  • Radiometric Correction of Terrestrial Li... (by )
  • Global and Local Sparse Subspace Optimiz... (by )
  • Automatic Detection of Building Points f... (by )
  • Inheriting Texture Maps Between Differen... (by )
  • 3D Visualisation of Underground Pipeline... (by )
  • Isprs Benchmark for Multi-platform Photo... (by )
  • Improving Semantic Updating Method on 3D... (by )
Scroll Left
Scroll Right

 



Copyright © World Library Foundation. All rights reserved. eBooks from World eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.