Proceedings of the 2015 AASRI International Conference on Circuits and Systems

Change Detection in Remote Sensing Images of Damage Areas with Complex Terrain Using Texture Information and SVM

Authors
Gao Fei, Zhang Lu, Wang Jun, Mei Jingyuan
Corresponding Author
Gao Fei
Available Online August 2015.
DOI
https://doi.org/10.2991/cas-15.2015.54How to use a DOI?
Keywords
change detection; support vector machines; texture feature; earthquake; damage areas; complex terrain.
Abstract
Traditional methods, when applied to change detection in remote sensing images of damage areas with complex terrain, often result in inaccuracy. And it is difficult to select an appropriate threshold, which however can be solved by support vector machines (SVM) method. Conventionally spectral information is put into SVM as its features; however, the experimental results are not satisfactory. Considering the spatial distribution and structure information of the image, we choose texture information as the new feature. In this paper, a change detection architecture based on the inclusion of texture information and SVM is proposed. Three textures including simple texture, Tamura texture and GLCM texture are adopted in the experiments. By calculating and comparatively analysing four accuracy specifications which are detection rate, missed alarm rate, false alarm rate and Kappa coefficient, we conclude with the experimental results that GLCM texture accurately reflects the diversity of the regional spatial distribution. The inclusion of appropriate texture information and SVM can be successfully adopted to change detection of damage areas with complex terrain.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2015 AASRI International Conference on Circuits and Systems (CAS 2015)
Part of series
Advances in Computer Science Research
Publication Date
August 2015
ISBN
978-94-62520-74-5
ISSN
2352-538X
DOI
https://doi.org/10.2991/cas-15.2015.54How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Gao Fei
AU  - Zhang Lu
AU  - Wang Jun
AU  - Mei Jingyuan
PY  - 2015/08
DA  - 2015/08
TI  - Change Detection in Remote Sensing Images of Damage Areas with Complex Terrain Using Texture Information and SVM
BT  - 2015 AASRI International Conference on Circuits and Systems (CAS 2015)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/cas-15.2015.54
DO  - https://doi.org/10.2991/cas-15.2015.54
ID  - Fei2015/08
ER  -