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http://dx.doi.org/10.7319/kogsis.2012.20.3.065

An Analysis of Land Cover Classification Methods Using IKONOS Satellite Image  

Kang, Nam Yi (전북대학교 토목공학과)
Pak, Jung Gi (전북대학교 토목공학과학과)
Cho, Gi Sung (전북대학교 토목공학과)
Yeu, Yeon (석곡관측과학기술연구원)
Publication Information
Journal of Korean Society for Geospatial Information Science / v.20, no.3, 2012 , pp. 65-71 More about this Journal
Abstract
Recently the high-resolution satellite images are helpfully using the land cover, status data for the natural resources or environment management. The effective satellite analysis process for these satellite images that require high investment can be increase the effectiveness has become increasingly important. In this Study, the statistical value of the training data is calculated and analyzed during the preprocessing. Also, that is explained about the maximum likelihood classification of traditional classification method, artificial neural network (ANN) classification method and Support Vector Machines(SVM) classification method and then the IKONOS high-resolution satellite imagery was produced the land cover map using each classification method. Each result data had to analyze the accuracy through the error matrix. The results of this study prove that SVM classification method can be good alternative of the total accuracy of about 86% than other classification method.
Keywords
IKONOS Satellite Image; Training Data; Land Cover Classification Method; Error Matrix;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Web., 2011, 국가 GIS 교육센터, http://www,e-gis,go,kr
2 과학기술부, 2010, 국가과학기술지식정보서비스.
3 일본 리모트센싱 연구회, 1998, 원격탐사개론.
4 김종호, 2004, SVM을 이용한 객체영상의 계층적 분류, 석사학위논문, 인하대학교.
5 노영희, 2009, 공간해상도와 분광해상도의 선택적 반영이 가능한 웨이블릿 기반 영상융합 기법 연구, 석사학위논문, 성신여자대학교.
6 사공호상, 임정호 2002, IKONOS 위성영상을 이용한 토지이용 현황 분석에 관한 연구, 한국 지리정보 추계 학술대회 발표논문집, 한국지리정보학회, pp. 35-43.
7 양옥진, 2001, 인공신경망의 알고리즘에 의한 토지적합성 분석, 석사학위논문, 조선대학교.
8 에르뎅솜배술드, 2012, Landsat 위성영상을 이용한 몽골 Tuv지역 토지피복변화, 석사학위논문, 전북대학교
9 전영준, 김진일 2003, IKONOS 위성영상의 토지피복 분석기법, 산업기술연구지 제 17권, pp. 135-144.
10 유성곤, 1999, 유전자 알고리즘을 이용한 원격탐사자료의 감독분류기법 연구, 석사학위논문, 전북대학교.
11 전영준, 김진일 2003, 고해상도 위성영상을 위한 감독분류시스템, 한국정보과학회, Vol 9, No3, pp. 301-310.   과학기술학회마을
12 Carpenter, G.A., Gjaja, M.N., Gopal, S., Woodcock, C.E., 1997, ART neural networks for remote sensing: vegetation classification from Landsat TM and terrain data, Geoscience and Remote Sensing, IEEE, vol 35, No2, pp. 308-325.   DOI
13 Davis, C.H., Xiangyun Wang, 2002, Urban land cover classification from high resolution multi-spectral IKONOS imagery, Geoscience and Remote Sensing Symposium, 2002. IGARSS, IEEE, Vol 2, pp.1204-1206.
14 Richards, J.A., 1993, Introduction to remote sensing digital image analysis, Spring-Verlag, Berlin.
15 Han, J., Lee, S., Chi, K., Ryu, K., 2002, Comparison of neuro-fuzzy, neural network, and maximum likelihood classifiers for land cover classification using IKONOS multispectral data, Geoscience and Remote Sensing Symposium, IEEE, Vol 9, pp. 3471-3473.
16 Huang, C., Davis, L.S., Townshend, J.R.G., 2002, An assessment of support vector machines for land cover classification, Int J Remote sensing, vol23, No4, pp. 725-749.   DOI
17 Pierce L., Samples G., Dobson M.C. and Ulaby, F., 1998, An automated unsupervised/supervised classification methodology, Geoscience and Remote Sensing Symposium Proceedings, IEEE, Vol. 4, pp. 1781-1783.
18 Sugumaran, R., Pavuluri, M.K., Zerr, D., 2003, The Use of High-Resolution Imagery for Identification of Urban Climax Forest Species Using Traditional and Rule-Based Classification Approach and Rule-based Classification Approach, Geoscience and Remote Sensing, IEEE, Vol 41, No 9, pp. 1933-1939.   DOI