Next Article in Journal
Use of Remote Sensing to Support Forest and Wetlands Policies in the USA
Next Article in Special Issue
Toronto’s Urban Heat Island—Exploring the Relationship between Land Use and Surface Temperature
Previous Article in Journal / Special Issue
Development of a New Ground Truth Database for Global Urban Area Mapping from a Gazetteer
Article Menu

Article Versions

Export Article

Open AccessArticle
Remote Sens. 2011, 3(6), 1188-1210; doi:10.3390/rs3061188

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

Geo-Environmental Cartography and Remote Sensing Research Group, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Received: 24 March 2011 / Revised: 5 May 2011 / Accepted: 1 June 2011 / Published: 14 June 2011
(This article belongs to the Special Issue Urban Remote Sensing)
View Full-Text   |   Download PDF [1652 KB, uploaded 19 June 2014]   |  

Abstract

In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered. View Full-Text
Keywords: building detection; LiDAR; high spatial resolution imagery; object-based image classification building detection; LiDAR; high spatial resolution imagery; object-based image classification
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Hermosilla, T.; Ruiz, L.A.; Recio, J.A.; Estornell, J. Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data. Remote Sens. 2011, 3, 1188-1210.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top