Browse > Article
http://dx.doi.org/10.7780/kjrs.2014.30.2.4

A study of Landcover Classification Methods Using Airborne Digital Ortho Imagery in Stream Corridor  

Kim, Young-Jin (Korea Environment Institute)
Cha, Su-Young (Industrial Development Research Institute, Kongju National University)
Cho, Yong-Hyeon (Department of Landscape Architecture, Kongju National University)
Publication Information
Korean Journal of Remote Sensing / v.30, no.2, 2014 , pp. 207-218 More about this Journal
Abstract
The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.
Keywords
airborne RGB and CIR digital ortho imagery; land cover classification; supervised classification algorithms; skewness; filtering; stream corridor;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 Landis, J.R. and G.G. Koch, 1977. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers, Biometrics: 363-374.
2 Sagong, H.S. and J.H. Im, 2003. An Empirical Study on the Land Cover Classification Method using IKONOS Image, Journal of the Korean Association of Geographic Information Studies, 6(3): 107-116.   과학기술학회마을
3 Thomas, N., C. Hendrix, and R.G. Congalton, 2003. A comparison of urban mapping methods using high-resolution digital imagery, Photogrammetric Engineering and Remote Sensing, 69(9): 963-972.   DOI
4 Kim, S.W., 2003. Preparation of land cover and use map by object oriented classification of IKONOS imagery, Master's Thesis, Korea University.
5 Ku, C.Y., 2007. Generation of the land cover map with high resolution satellite image, The Geographical Journal of Korea, 41(1): 83-94.
6 Lee, B.Y., C.H. Lee, S.W. Lee, and D. Ha, 2009. Application of the high resolution aerial images to estimate nonpoint pollution loads in the unit load appreach, Journal of Environmental Impact Assessment, 18(5): 281-291.
7 Lee, G.S., H.S. Lee, H.S. Chae, and E.H. Hwang, 2007. Landuse analysis of urban-stream using aerial image, Journal of the Korean Society of Civil Engineers, 27(3): 351-357.   과학기술학회마을
8 Lee, H.J., R.J. Ho, and Y.Y. Geol, 2010. Extracting High Quality Thematic Information by Using High-Resoultion Satellite Imagery, Journal of the Korean Society for Geo-Spatial Information System, 18(1): 73-81.   과학기술학회마을
9 Lee, H.J., J.H. Lu, and S.Y. Kim, 2011. Land Cover Object-oriented Base Classification Using Digital Aerial Photo Image, Journal of the Korean Society for Geo-Spatial Information System, 19(1): 105-113.   과학기술학회마을
10 Lee, S.M., 2011. A Study on Using High-Resolution Aerial Photos of Land Cover Classification Characteristics, Master's Thesis, Kyonggi University.
11 Lim, H.Y., 2005. Comparison of hyperspectral image classification techniques, Master's Thesis, Kyungpook National University
12 McCaffrey, T.M. and S.E. Franklin, 1993. Automated training site selection for large-area remote-sensing image analysis, Computers & Geosciences, 19(10): 1413-1428.   DOI   ScienceOn
13 Park, G.A., M.S. Lee, H.J. Kim, and S.J. Kim, 2004. Analysis of River Channel Morphology and Riparian Land Use Changes Using Aerial Photographs, Journal of the Korean Society of Civil Engineers, 24(5D): 815-821.   과학기술학회마을
14 Foody, G.M. and A. Mathur, 2004. Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification, Remote Sensing of Environment, 93(1): 107-117.   DOI   ScienceOn
15 Jackson, Q. and D.A. Landgrebe, 2001. An adaptive classifier design for high-dimensional data analysis with a limited training data set, IEEE Transactions on Geoscience and Remote Sensing, 39(12): 2664-2679.   DOI   ScienceOn
16 Buchheim, M. and T. Lillesand, 1989. Semi-automated training field extraction and analysis for efficient digital image classification, Photogrammetric Engineering and Remote Sensing, 55(9): 1347-1355.
17 Chun, K.W., K.N. Kim, and D.S. Cha, 1995. Study on channel-bed fluctuation using aerial photographs-analysis of spatial and temporal distribution on the deposits, Journal of Korean Forestry Society, 84(3): 369-376.
18 Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data, Remote Sensing of Environment, 37(1): 35-46.   DOI   ScienceOn
19 Jang, D.H. and M.K. Kim, 2003. Improving of landcover map using IKONOS image data, Journal of Geographic Information System Association of Korea, 11(2): 101-117.
20 Jensen, J.R., 2005. Introductory digital image processing: a remote sensing perspective, Prentice Hall.
21 Kang, J.M., J.S. Lee, J.B. Kim, and C. Zhang, 2009. A study to compare SVM with maximum likelihood classification using the high resolution satellite imagery, Proc. of Conference of the Korean Society of Civil Engineers, 1563-1566.
22 Woo, H.U., 2004. Land cover classification using high resolution satellite image and airborne laser scan data, Master's Thesis, Chungbuk National University.