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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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The remote sensing technique using SAR data have many advantages when applied to the disaster site due to its wide coverage and all-weather acquisition availability. Although a single-pol (polarimetric) SAR image cannot represent the land surface better than a quad-pol SAR image can, single-pol SAR data are worth using for disaster-induced change detection. In this paper, an automatic change detection method based on a mixture of GGDs (generalized Gaussian distribution) is proposed, and usability of the textural features and intensity is evaluated by using the proposed method. Three ALOS/PALSAR images were used in the experiments, and the study site was Norita City, which was affected by the 2011 Tohoku earthquake. The experiment results showed that the proposed automatic change detection method is practical for disaster sites where the large areas change. The intensity information is useful for detecting disaster-induced changes with a 68.3% g-mean, but the texture information is not. The autocorrelation and correlation show the interesting implication that they tend not to extract agricultural areas in the change detection map. Therefore, the final tsunami-induced change map is produced by the combination of three maps: one is derived from the intensity information and used as an initial map, and the others are derived from the textural information and used as auxiliary data.
목차
Abstract
1. Introduction
2. Methodology
3. Results
4. Conclusion
References
참고문헌 (27)
참고문헌 신청
Ainsworth, T.L., Kelly, J.P., and Lee, J.-S. (2009),Classification comparisons between dual-pol, compactpolarimetric and quad-pol SAR imagery, ISPRS Journalof Photogrammetry and Remote Sensing, Vol. 64, No. 5,pp. 464-471.
Bazi, Y., Bruzzone, L., and Melgainim, F. (2007), Imagethresholding based on the EM algorithm and thegeneralized Gaussian distribution, Pattern Recognition,Vol. 40, No. 2, pp. 619-634.
Bazi, Y., Melgani, F., Bruzzone, L., and Vernazza, G.(2009), A genetic expectation-maximization method forunsupervised change detection in multitemporal SARimagery, International Journal of Remote Sensing, Vol.30, No. 24, pp. 6591-6610.
Bovolo, F. and Bruzzone, L. (2007), A split-based approach tounsupervised change detection in large-size multitemporalimages: Application to tsunami-damage assessment, IEEETransactions on Geoscience and Remote Sensing, Vol. 45,No. 6, pp. 1658-1670.
Bovolo, F., Marin, C., and Bruzzone, L. (2013), A hierarchicalapproach to change detection in very high resolution SARimages for surveillance applications, IEEE Transactionson Geoscience and Remote Sensing, Vol. 51, No. 4, pp.2042-2054.
Bazi, Y., Bruzzone, L., and Melgainim, F. (2007), Imagethresholding based on the EM algorithm and thegeneralized Gaussian distribution, Pattern Recognition,Vol. 40, No. 2, pp. 619-634.
Bazi, Y., Melgani, F., Bruzzone, L., and Vernazza, G.(2009), A genetic expectation-maximization method forunsupervised change detection in multitemporal SARimagery, International Journal of Remote Sensing, Vol.30, No. 24, pp. 6591-6610.
Bovolo, F. and Bruzzone, L. (2007), A split-based approach tounsupervised change detection in large-size multitemporalimages: Application to tsunami-damage assessment, IEEETransactions on Geoscience and Remote Sensing, Vol. 45,No. 6, pp. 1658-1670.
Bovolo, F., Marin, C., and Bruzzone, L. (2013), A hierarchicalapproach to change detection in very high resolution SARimages for surveillance applications, IEEE Transactionson Geoscience and Remote Sensing, Vol. 51, No. 4, pp.2042-2054.
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UCI(KEPA) : I410-ECN-0101-2016-533-002817642