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http://dx.doi.org/10.7780/kjrs.2020.36.5.2.11

Accuracy Assessment of Unsupervised Change Detection Using Automated Threshold Selection Algorithms and KOMPSAT-3A  

Lee, Seung-Min (Department of Geoinformatics Engineering, Namseoul University)
Jeong, Jong-Chul (Department of Geoinformatics Engineering, Namseoul University)
Publication Information
Korean Journal of Remote Sensing / v.36, no.5_2, 2020 , pp. 975-988 More about this Journal
Abstract
Change detection is the process of identifying changes by observing the multi-temporal images at different times, and it is an important technique in remote sensing using satellite images. Among the change detection methods, the unsupervised change detection technique has the advantage of extracting rapidly the change area as a binary image. However, it is difficult to understand the changing pattern of land cover in binary images. This study used grid points generated from seamless digital map to evaluate the satellite image change detection results. The land cover change results were extracted using multi-temporal KOMPSAT-3A (K3A) data taken by Gimje Free Trade Zone and change detection algorithm used Spectral Angle Mapper (SAM). Change detection results were presented as binary images using the methods Otsu, Kittler, Kapur, and Tsai among the automated threshold selection algorithms. To consider the seasonal change of vegetation in the change detection process, we used the threshold of Differenced Normalized Difference Vegetation Index (dNDVI) through the probability density function. The experimental results showed the accuracy of the Otsu and Kapur was the highest at 58.16%, and the accuracy improved to 85.47% when the seasonal effects were removed through dNDVI. The algorithm generated based on this research is considered to be an effective method for accuracy assessment and identifying changes pattern when applied to unsupervised change detection.
Keywords
Change detection; Spectral Angle Mapper; Automated threshold selection algorithms; Grid points; KOMPSAT-3A;
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Times Cited By KSCI : 12  (Citation Analysis)
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1 Boardman, J. W., 1993. Spectral angle mapping: a rapid measure of spectral similarity, AVIRIS, Delivery by Ingenta, 1993.
2 Bovolo, F., S. Marchesi, and L. Bruzzone, 2012. A framework for automatic and unsupervised detection of multiple changes in multitemporal images, IEEE Transactions on Geoscience and Remote Sensing, 50(6): 2196-2212.   DOI
3 Choi, J., H. Park, D. Kim, and S. Choi, 2018. Unsupervised Change Detection of KOMPSAT-3 Satellite Imagery Based on Cross-sharpened Images by Guided Filter, Korean Journal of Remote Sensing, 34(5): 777-786 (in Korean with English abstract).   DOI
4 Choi, J., 2015. Unsupervised Change Detection for Very High-spatial Resolution Satellite Imagery by Using Object-based IR-MAD Algorithm, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 33(4): 297-304 (in Korean with English abstract).   DOI
5 Jeong, J.C., 2019. Evaluation of Grid-Based ROI Extraction Method Using a Seamless Digital Map, Journal of Cadastre & Land InformatiX, 49(1): 103-112 (in Korean with English abstract).   DOI
6 Jin, S., L. Yang, P. Danielson, C. Homer, J. Fry, and G. Xian, 2013. A Comprehensive Change Detection Method for Updating the National Land Cover Database to Circa 2011, Remote Sensing of Environment, 132: 159-175.   DOI
7 Kang, M. and Y. Lim, 2017. A Study on User Requirements for Satellite Development and Applications for National Land Observation, Journal of Korean Society for Geospatial Information Science, 25(4): 65-72 (in Korean with English abstract).   DOI
8 Kapur, J., P. Sahoo, and A. Wong, 1985. A new method for gray-level picture thresholding using the entropy of the histogram, Computer Vision, Graphics, and Image Processing, 29(3): 273-285.   DOI
9 KARI and SIIS (Korea aerospace research institute, SI imaging services), 2019. KOMPSAT-3A Product Specifications: Image Data Manual V1.5, http://www.si-imaging.com/resources/?pageid=2&uid=337&mod=document, Accessed on Sep. 9, 2020.
10 Kittler, J. and J. Illingworth, 1986. Minimum error thresholding, Pattern Recognition, 19(1): 41-47.   DOI
11 Kodama, S. and Y. Yamaguchi, 2010. Mapping of hydrothermally altered rocks using Modified Spectral Angle Mapper (MSAM) method and ASTER SWIR data, International Journal of Geoinformatics, 6(1): 41-53.
12 Lee, S., 2010. A Target Segmentation Method Based on Multi-Sensor/Multi-Frame, Journal of the Korea Institute of Military Science and Technology, 13(3): 445-452 (in Korean with English abstract).
13 Lim, S., D. Kim, C. Chung, and T. Han, 2006. Enhanced Preprocessing Algorithm for Image Code Recognition, Proc. of the Korean Information Science Society Conference, Seoul, Oct. 20-Oct. 21, 33(2B), pp. 51-54.
14 Liu, Q., L. Liu, and Y. Wang, 2017. Unsupervised Change Detection for Multispectral Remote Sensing Images Using Random Walks, Remote Sensing, 9(5): 438.   DOI
15 Mohsen, G. and A. Vahid, 2015. Generalized minimumerror thresholding for unsupervised change detection from multilook polarimetric SAR data, Proc. of 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, IT, Jul. 26-31, pp. 1853-1856.
16 Molina, I., E. Martinez, A. Arquero, G. Pajares, and J. Sanchez, 2012. Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes, Sensors, 12(3): 3528-3561.   DOI
17 Park, H., J. Choi, and J. Oh, 2018. Seasonal Effects Removal of Unsupervised Change Detection based Multitemporal Imagery, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 36(2): 51-58 (in Korean with English abstract).   DOI
18 MOTIE (Ministry of Trade, Industry and Energy in Korea), 2009. About us, https://www.motie.go.kr/ftz/gimje/freeTradeArea/businessOverview/businessOverview.jsp, Accessed on Jul. 14, 2020.
19 Oh, J. and C. Lee, 2015. Urban Change Detection Between Heterogeneous Images Using the Edge Information, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 33(4): 259-266. (in Korean with English abstract).   DOI
20 Otsu, N., 1979. A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66.   DOI
21 Park, N., K. Chi, K. Lee, and B. Kwon, 2003. Automatic Estimation of Threshold Values for Change Detection of Multi-temporal Remote Sensing Images, Korean Journal of Remote Sensing, 19(6): 465-478 (in Korean with English abstract).   DOI
22 Park, J., S. Oh, K. Park, P. Foucher, J. Jang, M. Lee, and T. Kim, 2017. The Ship Detection Using Airborne and In-situ Measurements Based on Hyperspectral Remote Sensing, Journal of the Korean Earth Science Society, 38(7): 535-545 (in Korean with English abstract).   DOI
23 Renza, D., E. Martinez, and A. Arquero, 2012. A New Approach to Change Detection in Multispectral Images by Means of ERGAS Index, IEEE Geoscience and Remote Sensing Letters, 10(1): 76-80.   DOI
24 Wang, B., J. Choi, S. Choi, S. Lee, P. Wu, and Y. Gao, 2017. Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images, Remote Sensing, 9(8): 804.   DOI
25 Renza, D., E. Martinez, I. Molina, and D. Ballesteros, 2017. Unsupervised change detection in a particular vegetation land cover type using Spectral Angle Mapper, Advanced in Space Research, 59(8): 2019-2031.   DOI
26 Sunwoo, W., D. Kim, S. Kim, and M. Choi, 2017. West seacoast wetland monitoring using KOMPSAT series imageries in high spatial resolution, Journal of Korea Water Resource Association, 50(6): 429-440 (in Korean with English abstract).   DOI
27 Teng, S., Y. Chen, K. Cheng, and H. Lo, 2008. Hypothesis-test-based landcover change detection using multitemporal satellite images - a comparative study, Advances in Space Research, 41(11): 1744-1754.   DOI
28 Tucker, C.J., 1979. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, 8(2): 127-150.   DOI
29 Tsai, W., 1985. Moment-preserving thresholding, Computer Vision, Graphics, and Image Processing, 29(3): 377-393.   DOI
30 Wang, B., S. Choi, J. Choi, S. Yang, Y. Byun, and K. Park, 2013. Comparison of Change Detection Accuracy based on VHR images Corresponding to the Fusion Estimation Indexes, Journal of Korean Society for Geospatial Information System, 21(2): 63-69 (in Korean with English abstract).   DOI
31 Zhao, J., J. Yang, Z. Lu, P. Li, W. Liu, and L. Yang, 2017. Unsupervised Method of Change Detection in Multi-Temporal PolSAR Data Using a Test Statistic and an Improved K&I Algorithm, Applied Sciences, 7(12): 1297.   DOI
32 Wang, Lin., F. Zhang, Y. Jing, X. Jiang, S. Yang, and X. Han, 2014. Multi-Temporal Detection of Rice Phenological Stages Using Canopy Spectrum, Rice Science, 21(2): 108-115.   DOI
33 Yang, S., J. Choi, and K. Yu, 2009. Development of the Digital Map Updating System using CAD Object Extracted from As-Built Drawings, Journal of the Korean Society for Geospatial Information Science, 17(3): 13-21 (in Korean with English abstract).