Remote Sensing Applications and Decision Support

Object-based classification of earthquake damage from high-resolution optical imagery using machine learning

[+] Author Affiliations
James Bialas, Thomas Oommen

Michigan Technological University, Geological and Mining Engineering and Sciences, 1400 Townsend Drive, Houghton, Michigan 49931, United States

Umaa Rebbapragada

Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, California 91109, United States

Eugene Levin

Michigan Technological University, School of Technology, 1400 Townsend Drive, Houghton, Michigan 49931, United States

J. Appl. Remote Sens. 10(3), 036025 (Sep 21, 2016). doi:10.1117/1.JRS.10.036025
History: Received February 10, 2016; Accepted August 30, 2016
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Abstract.  Object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

James Bialas ; Thomas Oommen ; Umaa Rebbapragada and Eugene Levin
"Object-based classification of earthquake damage from high-resolution optical imagery using machine learning", J. Appl. Remote Sens. 10(3), 036025 (Sep 21, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.036025


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