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http://dx.doi.org/10.7780/kjrs.2017.33.6.2.4

Unsupervised Change Detection Based on Sequential Spectral Change Vector Analysis for Updating Land Cover Map  

Park, Nyunghee (Department of Civil Engineering, Chungbuk National University)
Kim, Donghak (Department of Civil Engineering, Chungbuk National University)
Ahn, Jaeyoon (School of Civil Engineering, Chungbuk National University)
Choi, Jaewan (Department of Civil Engineering, Chungbuk National University)
Park, Wanyong (Agency for Defense Development)
Park, Hyunchun (Agency for Defense Development)
Publication Information
Korean Journal of Remote Sensing / v.33, no.6_2, 2017 , pp. 1075-1087 More about this Journal
Abstract
In this study, we tried to utilize results of the change detection analysis for satellite images as the basis for updating the land cover map. The Sequential Spectral Change Vector Analysis ($S^2CVA$) was applied to multi-temporal multispectral satellite imagery in order to extract changed areas, efficiently. Especially, we minimized the false alarm rate of unsupervised change detection due to the seasonal variation using the direction information in $S^2CVA$. The binary image, which is the result of unsupervised change detection, was integrated with the existing land cover map using the zonal statistics. And then, object-based analysis was performed to determine the changed area. In the experiment using PlanetScope data and the land cover map of the Ministry of Environment, the change areas within the existing land cover map could be detected efficiently.
Keywords
Change detection; Land cover map; PlanetScope; S2CVA;
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Times Cited By KSCI : 4  (Citation Analysis)
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