Remote Sensing Applications and Decision Support

Performance evaluation of random forest and support vector regressions in natural hazard change detection

[+] Author Affiliations
Vahid Eisavi

Tarbiat Modares University, Remote Sensing and Geographic Information System Department, Jalal Al Ahmad Street, No. 7, Tehran, Iran

Saeid Homayouni

University of Ottawa, Department of Geography, 75 Laurier Avenue E, Ottawa, Ontario K1N 6N5, Canada

J. Appl. Remote Sens. 10(4), 046030 (Dec 30, 2016). doi:10.1117/1.JRS.10.046030
History: Received August 3, 2016; Accepted December 6, 2016
Text Size: A A A

Abstract.  Information on land use and land cover changes is considered as a foremost requirement for monitoring environmental change. Developing change detection methodology in the remote sensing community is an active research topic. However, to the best of our knowledge, no research has been conducted so far on the application of random forest regression (RFR) and support vector regression (SVR) for natural hazard change detection from high-resolution optical remote sensing observations. Hence, the objective of this study is to examine the use of RFR and SVR to discriminate between changed and unchanged areas after a tsunami. For this study, RFR and SVR were applied to two different pilot coastlines in Indonesia and Japan. Two different remotely sensed data sets acquired by Quickbird and Ikonos sensors were used for efficient evaluation of the proposed methodology. The results demonstrated better performance of SVM compared to random forest (RF) with an overall accuracy higher by 3% to 4% and kappa coefficient by 0.05 to 0.07. Using McNemar’s test, statistically significant differences (Z1.96), at the 5% significance level, between the confusion matrices of the RF classifier and the support vector classifier were observed in both study areas. The high accuracy of change detection obtained in this study confirms that these methods have the potential to be used for detecting changes due to natural hazards.

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

Citation

Vahid Eisavi and Saeid Homayouni
"Performance evaluation of random forest and support vector regressions in natural hazard change detection", J. Appl. Remote Sens. 10(4), 046030 (Dec 30, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.046030


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

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.