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Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data

Urbanization is commonly accepted as an important contributor to the growth of man-made structures and as a rapid convertor of natural environments to impervious surfaces. Roofs are one class of impervious surface whose materials can highly influence the quality of urban surface water. In this study...

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Main Authors: Hamedianfar, Alireza, Mohd Shafri, Helmi Zulhaidi, Mansor, Shattri, Ahmad, Noordin
Format: Article
Language:English
Published: Taylor & Francis 2014
Online Access:http://psasir.upm.edu.my/id/eprint/37983/1/Improving%20detailed%20rule-based%20feature%20extraction%20of%20urban%20areas%20from%20WorldView-2%20image%20and%20lidar%20data.pdf
http://psasir.upm.edu.my/id/eprint/37983/
http://www.tandfonline.com/doi/abs/10.1080/01431161.2013.879350
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spelling my.upm.eprints.379832016-01-28T05:16:24Z http://psasir.upm.edu.my/id/eprint/37983/ Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data Hamedianfar, Alireza Mohd Shafri, Helmi Zulhaidi Mansor, Shattri Ahmad, Noordin Urbanization is commonly accepted as an important contributor to the growth of man-made structures and as a rapid convertor of natural environments to impervious surfaces. Roofs are one class of impervious surface whose materials can highly influence the quality of urban surface water. In this study, two data sources, WorldView-2 (WV-2) imagery and a combination of WV-2 and lidar data, were utilized to map intra-urban targets, with 13 classes. Images were classified using object-based image analysis. Pixel-based classifications using the support vector machine (SVM) and maximum likelihood (ML) methods were also tested for their abilities to use both lidar data and WV-2 imagery. ML and SVM classifications yielded overall accuracies of 72.46% and 75.69%, respectively. The results of these classifiers exhibited mixed pixels and salt-and-pepper effects. Spectral, spatial, and textural attributes as well as various spectral indices were employed in the object-based classification of WV-2 imagery. Feature classification of WV-2 imagery resulted in 85% overall accuracy. Lidar data were added to WV-2 imagery to assist in the spatial and spectral diversities of urban infrastructures. Classified image made from WV-2 imagery and lidar data achieved 92.84% overall accuracy. Rule-sets of these fused datasets effectively reduced the spectral variation and spatial heterogeneities of intra-urban classes, causing finer boundaries among land-cover classes. Therefore, object-based classification of WV-2 imagery and lidar data efficiently improved detailed characterization of roof types and other urban surface materials. Taylor & Francis 2014 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/37983/1/Improving%20detailed%20rule-based%20feature%20extraction%20of%20urban%20areas%20from%20WorldView-2%20image%20and%20lidar%20data.pdf Hamedianfar, Alireza and Mohd Shafri, Helmi Zulhaidi and Mansor, Shattri and Ahmad, Noordin (2014) Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data. International Journal of Remote Sensing, 35 (5). pp. 1876-1899. ISSN 0143-1161; ESSN: 1366-5901 http://www.tandfonline.com/doi/abs/10.1080/01431161.2013.879350 10.1080/01431161.2013.879350
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Urbanization is commonly accepted as an important contributor to the growth of man-made structures and as a rapid convertor of natural environments to impervious surfaces. Roofs are one class of impervious surface whose materials can highly influence the quality of urban surface water. In this study, two data sources, WorldView-2 (WV-2) imagery and a combination of WV-2 and lidar data, were utilized to map intra-urban targets, with 13 classes. Images were classified using object-based image analysis. Pixel-based classifications using the support vector machine (SVM) and maximum likelihood (ML) methods were also tested for their abilities to use both lidar data and WV-2 imagery. ML and SVM classifications yielded overall accuracies of 72.46% and 75.69%, respectively. The results of these classifiers exhibited mixed pixels and salt-and-pepper effects. Spectral, spatial, and textural attributes as well as various spectral indices were employed in the object-based classification of WV-2 imagery. Feature classification of WV-2 imagery resulted in 85% overall accuracy. Lidar data were added to WV-2 imagery to assist in the spatial and spectral diversities of urban infrastructures. Classified image made from WV-2 imagery and lidar data achieved 92.84% overall accuracy. Rule-sets of these fused datasets effectively reduced the spectral variation and spatial heterogeneities of intra-urban classes, causing finer boundaries among land-cover classes. Therefore, object-based classification of WV-2 imagery and lidar data efficiently improved detailed characterization of roof types and other urban surface materials.
format Article
author Hamedianfar, Alireza
Mohd Shafri, Helmi Zulhaidi
Mansor, Shattri
Ahmad, Noordin
spellingShingle Hamedianfar, Alireza
Mohd Shafri, Helmi Zulhaidi
Mansor, Shattri
Ahmad, Noordin
Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data
author_facet Hamedianfar, Alireza
Mohd Shafri, Helmi Zulhaidi
Mansor, Shattri
Ahmad, Noordin
author_sort Hamedianfar, Alireza
title Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data
title_short Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data
title_full Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data
title_fullStr Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data
title_full_unstemmed Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data
title_sort improving detailed rule-based feature extraction of urban areas from worldview-2 image and lidar data
publisher Taylor & Francis
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/37983/1/Improving%20detailed%20rule-based%20feature%20extraction%20of%20urban%20areas%20from%20WorldView-2%20image%20and%20lidar%20data.pdf
http://psasir.upm.edu.my/id/eprint/37983/
http://www.tandfonline.com/doi/abs/10.1080/01431161.2013.879350
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score 13.211869

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