Object-based classification of earthquake damage from high-resolution optical imagery using machine learning
Document Type
Article
Publication Date
9-21-2016
Department
Center for Data Sciences; Department of Geological and Mining Engineering and Sciences
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.
Publication Title
Journal of Applied Remote Sensing
Recommended Citation
Bialas, J.,
Oommen, T.,
Rebbapragada, U.,
&
Levin, E.
(2016).
Object-based classification of earthquake damage from high-resolution optical imagery using machine learning.
Journal of Applied Remote Sensing,
10(3).
http://doi.org/10.1117/1.JRS.10.036025
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/985
Publisher's Statement
© 2016 SPIE. Publisher's version of record: https://doi.org/10.1117/1.JRS.10.036025