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An earthquake damage identification approach from VHR image using mathematical morphology and machine learning

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Abstract

An accurate earthquake-induced damage assessment from very high resolution satellite images requires a joint use of spectral and spatial information. The spatial relations between pixels in damaged areas should properly be included in the classification model in order to exploit the spatial information. Morphological Profiles (MPs) and Attribute Profiles (APs) are the state-of-the-art image processing methods that produce a set of filtered images, which are able to highlight specific patterns representing a specific land cover class. In this study, the MPs and the APs are used to create additional spatial features for two different very high resolution post-event satellite images, acquired from the City of Bam in Iran and the City of Port-au-Prince in Haiti. These contextual features are then analyzed by means of a feature selection algorithm, called Minimum Redundancy Maximum Relevance, to find the most relevant features contributing the damage class the most. A final feature subset of selected features is analyzed using two different classifiers, which are k-nearest neighbors and support-vector machines. The results show that the use of a proper configuration of those profiles can significantly improve the classification accuracy and the quality of the thematic map, but generalization of the classification model is limited especially for the larger areas.

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Notes

  1. The authors are grateful to ITU Research and Application Center for Satellite Communications and Remote Sensing (CSCRS) for providing the pre- and post-earthquake QuickBird images covering the Port-au-Prince study area.

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Authors and Affiliations

  1. Institute of Informatics, Istanbul Technical University, 34469, Istanbul, Turkey

    Enes Oğuzhan Alataş

  2. Institute of Earthquake Engineering and Disaster Management, Istanbul Technical University, 34469, Istanbul, Turkey

    Gülşen Taşkın

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  1. Enes Oğuzhan Alataş
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  2. Gülşen Taşkın
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Correspondence to Enes Oğuzhan Alataş.

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Alataş, E.O., Taşkın, G. An earthquake damage identification approach from VHR image using mathematical morphology and machine learning. Neural Comput & Applic 34, 18757–18771 (2022). https://doi.org/10.1007/s00521-022-07452-6

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