New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images
The fine resolution of synthetic aperture radar (SAR) images enables the rapid detection of severely damaged areas in the case of natural disasters. Developing an optimal model for detecting damage in multitemporal SAR intensity images has been a focus of research. Recent studies have shown that com...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/10/12/2059 |
| _version_ | 1818971603733577728 |
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| author | Yukio Endo Bruno Adriano Erick Mas Shunichi Koshimura |
| author_facet | Yukio Endo Bruno Adriano Erick Mas Shunichi Koshimura |
| author_sort | Yukio Endo |
| collection | DOAJ |
| description | The fine resolution of synthetic aperture radar (SAR) images enables the rapid detection of severely damaged areas in the case of natural disasters. Developing an optimal model for detecting damage in multitemporal SAR intensity images has been a focus of research. Recent studies have shown that computing changes over a moving window that clusters neighboring pixels is effective in identifying damaged buildings. Unfortunately, classifying tsunami-induced building damage into detailed damage classes remains a challenge. The purpose of this paper is to present a novel multiclass classification model that considers a high-dimensional feature space derived from several sizes of pixel windows and to provide guidance on how to define a multiclass classification scheme for detecting tsunami-induced damage. The proposed model uses a support vector machine (SVM) to determine the parameters of the discriminant function. The generalization ability of the model was tested on the field survey of the 2011 Great East Japan Earthquake and Tsunami and on a pair of TerraSAR-X images. The results show that the combination of different sizes of pixel windows has better performance for multiclass classification using SAR images. In addition, we discuss the limitations and potential use of multiclass building damage classification based on performance and various classification schemes. Notably, our findings suggest that the detectable classes for tsunami damage appear to differ from the detectable classes for earthquake damage. For earthquake damage, it is well known that a lower damage grade can rarely be distinguished in SAR images. However, such a damage grade is apparently easy to identify from tsunami-induced damage grades in SAR images. Taking this characteristic into consideration, we have successfully defined a detectable three-class classification scheme. |
| first_indexed | 2024-12-20T14:55:00Z |
| format | Article |
| id | doaj.art-377f4a1d9e4e4c38b18b1cceba3c08f4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T14:55:00Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-377f4a1d9e4e4c38b18b1cceba3c08f42022-12-21T19:36:52ZengMDPI AGRemote Sensing2072-42922018-12-011012205910.3390/rs10122059rs10122059New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR ImagesYukio Endo0Bruno Adriano1Erick Mas2Shunichi Koshimura3Graduate School of Engineering, Tohoku University, Aoba-Ku, Sendai 980-8752, JapanGeoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Chuo-ku, Tokyo 103-0027, JapanInternational Research Institute of Disaster Science, Tohoku University, Aoba-Ku, Sendai 980-8752, JapanInternational Research Institute of Disaster Science, Tohoku University, Aoba-Ku, Sendai 980-8752, JapanThe fine resolution of synthetic aperture radar (SAR) images enables the rapid detection of severely damaged areas in the case of natural disasters. Developing an optimal model for detecting damage in multitemporal SAR intensity images has been a focus of research. Recent studies have shown that computing changes over a moving window that clusters neighboring pixels is effective in identifying damaged buildings. Unfortunately, classifying tsunami-induced building damage into detailed damage classes remains a challenge. The purpose of this paper is to present a novel multiclass classification model that considers a high-dimensional feature space derived from several sizes of pixel windows and to provide guidance on how to define a multiclass classification scheme for detecting tsunami-induced damage. The proposed model uses a support vector machine (SVM) to determine the parameters of the discriminant function. The generalization ability of the model was tested on the field survey of the 2011 Great East Japan Earthquake and Tsunami and on a pair of TerraSAR-X images. The results show that the combination of different sizes of pixel windows has better performance for multiclass classification using SAR images. In addition, we discuss the limitations and potential use of multiclass building damage classification based on performance and various classification schemes. Notably, our findings suggest that the detectable classes for tsunami damage appear to differ from the detectable classes for earthquake damage. For earthquake damage, it is well known that a lower damage grade can rarely be distinguished in SAR images. However, such a damage grade is apparently easy to identify from tsunami-induced damage grades in SAR images. Taking this characteristic into consideration, we have successfully defined a detectable three-class classification scheme.https://www.mdpi.com/2072-4292/10/12/2059synthetic aperture radarchange detectiontsunamibuildings |
| spellingShingle | Yukio Endo Bruno Adriano Erick Mas Shunichi Koshimura New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images Remote Sensing synthetic aperture radar change detection tsunami buildings |
| title | New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images |
| title_full | New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images |
| title_fullStr | New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images |
| title_full_unstemmed | New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images |
| title_short | New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images |
| title_sort | new insights into multiclass damage classification of tsunami induced building damage from sar images |
| topic | synthetic aperture radar change detection tsunami buildings |
| url | https://www.mdpi.com/2072-4292/10/12/2059 |
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