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http://dx.doi.org/10.7780/kjrs.2015.31.3.5

Development and Evaluation of a Texture-Based Urban Change Detection Method Using Very High Resolution SAR Imagery  

Kang, Ah-Reum (Satellite Information Promotion Team, Satellite Information Center, Korea Aerospace Research Institute(KARI))
Byun, Young-Gi (Satellite Information Promotion Team, Satellite Information Center, Korea Aerospace Research Institute(KARI))
Chae, Tae-Byeong (Satellite Information Promotion Team, Satellite Information Center, Korea Aerospace Research Institute(KARI))
Publication Information
Korean Journal of Remote Sensing / v.31, no.3, 2015 , pp. 255-265 More about this Journal
Abstract
Very high resolution (VHR) satellite imagery provide valuable information on urban change monitoring due to multi-temporal observation over large areas. Recently, there has been increased interest in the urban change detection technique using VHR Synthetic Aperture Radar (SAR) imaging system, because it can take images regardless of solar illumination and weather condition. In this paper, we proposed a texture-based urban change detection method using the VHR SAR texture features generated from Gray-Level Co-Occurrence Matrix (GLCM). In order to evaluate the efficiency of the proposed method, the result was compared, visually and quantitatively, with the result of Non-Coherent Change Detection (NCCD) which is widely used for the change detection of VHR SAR image. The experimental results showed the greater detection accuracy and the visually satisfactory result compared with the NCCD method. In conclusion, the proposed method has shown a great potential for the extraction of urban change information from VHR SAR imagery.
Keywords
Change Detection; SAR; GLCM; Texture image;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Celik, T., 2010. Change Detection in Satellite Images Using a Genetic Algorithm Approach, IEEE Geoscience and Remote Sensing, 7(2): 386-390.   DOI
2 Dellinger, F., J. Delon, Y. Gousseau, J. Michel, and F. Tupin, 2014. Change detection for high resolution satellite images, based on SIFT descriptors and an a contrario approach, IGARSS, Quebec City, QC, July. 13-18, pp. 1281-1284.
3 Dong, Y., B. Forster, and C. Ticehurst, 1997. Radar backscatter analysis for urban environment, International Journal of Remote Sensing, 18(6): 1351-1364.   DOI
4 Gong, M., Y. Li, L. Jiao, M. Jia, and L. Su, 2014. SAR change detection based on intensity and texture changes, ISPRS Journal of Photogrammetry and Remote Sensing, 93: 123-135.   DOI
5 Grey, W.M.F., A.J. Luckman, and D. Holland, 2003. Mapping urban change in the UK using satellite radar interferometry. Remote Sensing of Environment, 87(1): 16-22.   DOI
6 He, C., Y. Zhao, and A. Wei, 2010. Land-use/landcover change detection by using the extended change-vector analysis, Information Science and Engineering(ICISE), Hangzhou, China, Dec. 4-6, pp.3809-3812.
7 Huang, S.Q., D.Z. Liu, X.H. Cai, 2009. A New Change Detection Algorithm for SAR Images, Proc.of APSAR, Xian Shanxi, Oct. 26-30, pp. 729-732.
8 Jung, M.H., 2012. Early Disaster Damage Assessment Using Remotely Sensing Imagery:Damage Detection, Mapping and Estimation, The Institute of Electronics And Information Engineers, 49(2): 143-148.
9 Kandaswamy, U., D.A. Adjeroh, and M.C. Lee, 2005. Efficient texture analysis of SAR imagery, IEEE Geoscience and Remote Sensing, 43(9): 2075-2083.   DOI
10 Kim, G.H., S.P. Choi, W.S. Yook, and H.G. Sohn, 2005. Extraction of Urban Boundary Using Grey Level Co-Occurrence Matrix Method in Pancromatic satellite Imagery, Korean Society of Civil Engineers, 26(1) : 211-217.
11 Lee, W.K., 2015. Development of Change Detection Algorithm using High Resolution SAR Image, KARI Satellite Information Application Project, Korea.
12 Liao, M., L. Jiang, H. Lin, and D. Li, 2005. Urban change detection using coherence and intensity characteristics of multi-temporal ERS-1/2 Imagery, Fringe ATSR Workshop.
13 Liu, W. and F. Yamazaki, 2011. Urban monitoring and change detection of central tokyo using high-resolution X-band SAR images, Proc. of IGARSS, Vancouver, BC, July. 24-29, pp.2133-2136.
14 Matsuoka, M. and F. Yamazaki, 2004. Use of satellite SAR intensity imagery for detecting building areas damaged due to earthquakes, Earthquake Spectra, 20(3): 975-994.   DOI
15 Sirmacek, B. and C. Unsalan, 2009. Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory, IEEE Geoscience and Remote Sensing, 47(4): 1156-1167.   DOI
16 Moser, G. and S.B. Serpico, 2006. Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery, IEEE Geoscience and Remote Sensing, 44(10): 2972-2982.   DOI
17 Otsu, N., 1979. A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Sys., Man., Cyber. 9(1): 62-66.   DOI   ScienceOn
18 Rignot, E.M. and J.J. Van Zyl, 1993. Change detection techniques for ERS-1 SAR data, IEEE Geoscience and Remote Sensing, 31(4): 896-906.   DOI
19 Ulaby, F.T., F. Kouyate, B. Brisco, and T.L. Williams, 1986. Textural information in SAR images, IEEE Geoscience and Remote Sensing, 24(2): 235-245.