Image and Signal Processing Methods

Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data

[+] Author Affiliations
Sarah Hanim Samsudin

Universiti Putra Malaysia, Department of Civil Engineering, Faculty of Engineering, Serdang 43400, Selangor, Malaysia

Helmi Z. M. Shafri

Universiti Putra Malaysia, Department of Civil Engineering, Faculty of Engineering, Serdang 43400, Selangor, Malaysia

Universiti Putra Malaysia, Geospatial Information Science Research Centre, Serdang 43400, Selangor, Malaysia

Alireza Hamedianfar

Universiti Putra Malaysia, Department of Civil Engineering, Faculty of Engineering, Serdang 43400, Selangor, Malaysia

Islamic Azad University, Department of Surveying Engineering, Estahban Branch, Estahban, Iran

Islamic Azad University, Young Researchers and Elite Club, Estahban Branch, Estahban, Iran

J. Appl. Remote Sens. 10(2), 025021 (Jun 13, 2016). doi:10.1117/1.JRS.10.025021
History: Received January 19, 2016; Accepted May 20, 2016
Text Size: A A A

Abstract.  Status observations of roofing material degradation are constantly evolving due to urban feature heterogeneities. Although advanced classification techniques have been introduced to improve within-class impervious surface classifications, these techniques involve complex processing and high computation times. This study integrates field spectroscopy and satellite multispectral remote sensing data to generate degradation status maps of concrete and metal roofing materials. Field spectroscopy data were used as bases for selecting suitable bands for spectral index development because of the limited number of multispectral bands. Mapping methods for roof degradation status were established for metal and concrete roofing materials by developing the normalized difference concrete condition index (NDCCI) and the normalized difference metal condition index (NDMCI). Results indicate that the accuracies achieved using the spectral indices are higher than those obtained using supervised pixel-based classification. The NDCCI generated an accuracy of 84.44%, whereas the support vector machine (SVM) approach yielded an accuracy of 73.06%. The NDMCI obtained an accuracy of 94.17% compared with 62.5% for the SVM approach. These findings support the suitability of the developed spectral index methods for determining roof degradation statuses from satellite observations in heterogeneous urban environments.

Figures in this Article
© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Sarah Hanim Samsudin ; Helmi Z. M. Shafri and Alireza Hamedianfar
"Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data", J. Appl. Remote Sens. 10(2), 025021 (Jun 13, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.025021


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.