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http://dx.doi.org/10.7780/kjrs.2017.33.6.3.6

Change Detection of Urban Development over Large Area using KOMPSAT Optical Imagery  

Han, Youkyung (School of Convergence & Fusion System Engineering, Kyungpook National University)
Kim, Taeheon (School of Convergence & Fusion System Engineering, Kyungpook National University)
Han, Soohee (Department of Geoinformatics Engineering, Kyungil University)
Song, Jeongheon (Hypersensing Inc.)
Publication Information
Korean Journal of Remote Sensing / v.33, no.6_3, 2017 , pp. 1223-1232 More about this Journal
Abstract
This paper presents an approach to detect changes caused by urban development over a large area using KOMPSAT optical images. In order to minimize the radiometric dissimilarities between the images acquired at different times, we apply the grid-based rough radiometric correction as a preprocessing to detect changes in a large area. To improve the accuracy of the change detection results for urban development, we mask-out non-interest areas such as water and forest regions by the use of land-cover map provided by the Ministry of Environment. The Change Vector Analysis(CVA) technique is applied to detect changes caused by urban development. To confirm the effectiveness of the proposed approach, a total of three study sites from Sejong City is constructed by combining KOMPSAT-2 images acquired on May 2007 and May 2016 and a KOMPSAT-3 image acquired on March 2014. As a result of the change detection accuracy evaluation for the study site generated from the KOMPSAT-2 image acquired on May 2007 and the KOMPSAT-3 image acquired on March 2014, the overall accuracy of change detection was about 91.00%. It is demonstrated that the proposed method is able to effectively detect urban development changes in a large area.
Keywords
Change detection; KOMPSAT; urban development; change vector analysis;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Han, Y., F. Bovolo, and L. Bruzzone, 2017. Segmentationbased fine registration of very high resolution multitemporal images, IEEE Transactions on Geoscience and Remote Sensing, 55(5): 2884-2897.   DOI
2 Bovolo, F. and L. Bruzzone, 2007. A split-based approach to unsupervised change detection in large-size multitemporal images: Application to tsunami-damage assessment, IEEE Transactions on Geoscience and Remote Sensing, 45(6): 1658-1670.   DOI
3 Bruzzone, L. and D. Prieto, 2000. Automatic analysis of the difference image for unsupervised change detection, IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1171-1182.   DOI
4 Han, Y., F. Bovolo, and L. Bruzzone, 2017. Segmentationbased fine registration of very high resolution multitemporal images, IEEE Transactions on Geoscience and Remote Sensing, 55(5): 2884-2897.   DOI
5 Klaric, M. N., B. C. Claywell, G. J. Scott, N. J. Hudson, O. Sjahputera, Y. Li, and C. H. Davis, 2013. GeoCDX: An automated change detection and exploitation system for high-resolution satellite imagery, IEEE Transactions on Geoscience and Remote Sensing, 51(4): 2067-2086.   DOI
6 Liu, S., L. Bruzzone, F. Bovolo, M. Zanetti, and P. Du, 2015. Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, 53(8): 4363-4378.   DOI
7 Sunwoo, W., D. Kim, S. Kang, and M. Choi, 2016. Application of KOMSAT-2 imageries for change detection of land use and land cover in the West coasts of the Korean peninsula, Korean Journal of Remote Sensing, 32(2): 141-153 (in Korean with English abstract).   DOI
8 Tang, Y., X. Huang, and L. Zhang, 2013. Fault-tolerant building change detection from urban highresolution remote sensing imagery, IEEE geoscience and remote sensing letters, 10(5): 1060-1064.   DOI
9 Tian, J., S. Cui, and P. Reinartz, 2014. Building change detection based on satellite stereo imagery and digital surface models, IEEE Transactions on Geoscience and Remote Sensing, 52(1): 406-417.   DOI
10 Wen, D., X. Huang, L. Zhang, and J. A. Benediktsson, 2016. A novel automatic change detection method for urban high-resolution remotely sensed imagery based on multiindex scene representation, IEEE Transactions on Geoscience and Remote Sensing, 54(1): 609-625.   DOI