한국지리정보학회지 (Journal of the Korean Association of Geographic Information Studies)

  • 제20권1호
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  • Pages.1-14
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  • 2017
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  • 1226-9719(pISSN)
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  • 2287-6952(eISSN)
한국지리정보학회 (The Korean Association of Geographic Information Studies)
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UAV와 객체기반 영상분석 기법을 활용한 토지피복 분류 - 충청남도 서천군 마서면 일원을 대상으로 -

Land Cover Classification Using UAV Imagery and Object-Based Image Analysis - Focusing on the Maseo-myeon, Seocheon-gun, Chungcheongnam-do -

  • 문호경 (국립생태원 생태연구본부) ;
  • 이선미 (국립생태원 생태연구본부) ;
  • 차재규 (국립생태원 생태연구본부)
  • MOON, Ho-Gyeong (Bureau of Ecological Research, National Institute of Ecology) ;
  • LEE, Seon-Mi (Bureau of Ecological Research, National Institute of Ecology) ;
  • CHA, Jae-Gyu (Bureau of Ecological Research, National Institute of Ecology)
  • 투고 : 2016.11.02
  • 심사 : 2017.01.12
  • 발행 : 2017.03.31
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초록

토지피복도는 지역의 현황을 파악하는 기초적 자료이지만 시간적 공간적 해상도의 한계로 인하여 생태 연구 분야에서의 활용성은 떨어지는 측면이 있다. 이에 본 연구에서는 UAV으로 취득된 고해상도 영상을 기반으로 토지피복도 제작과 자료의 활용가능성을 알아보고자 하였다. UAV를 이용하여 연구대상지 $2.5km^2$ 범위에서 10.5cm 정사영상을 취득하였으며 객체기반(Object-based)과 화소기반(pixel-based) 분류를 통해 얻어진 토지피복도를 비교 분석하였다. 정확도 검증 결과 화소기반 분류는 Kappa 0.77, 객체기반 분류는 Kappa 0.82로 분류정확도가 높았으며, 전반적인 면적비율은 유사하지만 초지, 습지 지역에서 양호한 분류 결과가 나타났다. 객체기반 분류를 위한 최적의 영상분할 가중치는 Scale150, Shape 0.5, Compactness 0.5, Color 1로 선정하였으며 가중치 선정과정에서 Scale이 가장 큰 영향을 주었다. 화소기반 분류 결과와 비교해 객체간의 명확한 경계를 가지므로 결과물 판독이 용이한 것으로 나타났으며, 환경부 토지피복도(세분류)와 비교하여 개발지역(도로, 건물 등)을 제외한 자연지역(산림, 초지, 습지 등)의 분류에 효과적이었다. UAV 영상을 활용한 토지피복 분류방법으로서 객체기반 분류기법의 적용은 자료의 최신성, 정확성, 경제성 등의 장점으로 생태 연구 분야에 기여할 수 있을 것으로 판단된다.

A land cover map provides basic information to help understand the current state of a region, but its utilization in the ecological research field has deteriorated due to limited temporal and spatial resolutions. The purpose of this study was to investigate the possibility of using a land cover map with data based on high resolution images acquired by UAV. Using the UAV, 10.5 cm orthoimages were obtained from the $2.5km^2$ study area, and land cover maps were obtained from object-based and pixel-based classification for comparison and analysis. From accuracy verification, classification accuracy was shown to be high, with a Kappa of 0.77 for the pixel-based classification and a Kappa of 0.82 for the object-based classification. The overall area ratios were similar, and good classification results were found in grasslands and wetlands. The optimal image segmentation weights for object-based classification were Scale=150, Shape=0.5, Compactness=0.5, and Color=1. Scale was the most influential factor in the weight selection process. Compared with the pixel-based classification, the object-based classification provides results that are easy to read because there is a clear boundary between objects. Compared with the land cover map from the Ministry of Environment (subdivision), it was effective for natural areas (forests, grasslands, wetlands, etc.) but not developed areas (roads, buildings, etc.). The application of an object-based classification method for land cover using UAV images can contribute to the field of ecological research with its advantages of rapidly updated data, good accuracy, and economical efficiency.

키워드

참고문헌

  1. Choi, J.W. 2015. Thematic map generation by using multispectral imaery based on UAV. Journal of the Institute of Construction Technology 34(2):53-59 (최재완, 2015. UAV 기반 다중분광 영상을 활용한 주제도 생성 기법. 건설기술연구 34(2):53-59.
  2. Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37(1):35-46. https://doi.org/10.1016/0034-4257(91)90048-B
  3. Desclee, B., P. Bogaert, and P. Defourny. 2006. Forest change detection by statistical object-based method. Remote Sensing of Environment 102(1):1-11. https://doi.org/10.1016/j.rse.2006.01.013
  4. Duro, D.C., S.E. Franklin, and M.G. Dube. 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment 118:259-272. https://doi.org/10.1016/j.rse.2011.11.020
  5. Gay, A.P., T.P. Stewart, R. Angel, M. Easey, A. J. Eves, N.J. Thomas, D.A. Pearce, and A.I. Kemp. 2009. Developing unmanned aerial vehicles for local and flexible environmental and agricultural monitoring. Proceedings of RSPSoc 2009 Annual Conference. pp.471-476.
  6. Hussain, M., D. Chen, A. Cheng, H. Wei, and D. Stanley. 2013. Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing 80:91-106. https://doi.org/10.1016/j.isprsjprs.2013.03.006
  7. Kim, H.O. and J.M. Yeom. 2012. A study on object-based image analysis methods for land cover classification in agricultural areas. Journal of the Korean Association of Geographic Information Studies 15(4):26-41 (김현옥, 염종민. 2012. 농촌지역 토지피복분류를 위한 객체기반 영상 분석기법 연구. 한국지리정보학회지 15(4):26-41). https://doi.org/10.11108/kagis.2012.15.4.026
  8. Kim, J. B., M.G. Kim, and H.C. Yoon. 2011. Improvement of regulations for effective UAV operation in disaster detection. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography 29(5):509-517 (김종배, 김민규, 윤희천. 2011. 효율적인 재해탐지용 UAV 운용을 위한 현행 규정 개선방안. 한국측량학회지 29(5):509-517). https://doi.org/10.7848/ksgpc.2011.29.5.509
  9. Kim. S. G., Y.D. Sung, and G.O. Kim. 2012. A study on methods of utilizing unmanned aerial vehicle (UVA) in the area of spatial information 28(1):169-178 (김석구, 성연동, 김권욱. 2012. 공간정보 분야의 무인항공기(UAV) 활용방안에 관한 연구. 한국지적학회지 28(1):169-178).
  10. Lechner, A.M., A. Fletcher, and P. Erskine. 2012. Characterising upland swamps using object-based classification methods and hyper-spatial resolution imagery derived from an unmanned aerial vehicle. Remote Sensing and Spatial Information Sciences 1(4):101-106.
  11. Lee, G.S. and Y.W. Choi. 2014. Land cover classification of Nakdong River Basin using object-based image analysis methods. Journal of the Korean Association of Geographic Information Studies 16(3): 3-8 (이근상, 최연웅. 2014. 객체기반 영상 분석 기법에 의한 낙동강 유역 토지피복 분류. 한국지적정보학회지 16(3):3-8).
  12. Lee, G.S., Y.W. Choi, K.S. Jung, and G.S. Cho. 2015. Analysis of the spatial information accuracy according to photographing direction of fixed wing UAV. Journal of the Korean Association of Geographic Information Studies 17(3): 141-149 (이근상, 최연웅, 정관수, 조기성 2015. 고정익 UAV의 촬영방향에 따른 DEM 및 정사영상 제작 정확도 분석. 한국지적정보학회지 17(3):141-149).
  13. Lee, H.J., Y.G. Yu, and J.H. Ru. 2010. Improvement of accuracy in land cover information using an object-based classification. Journal of the Korean Society for Geo-Spatial Information System 18 (3):11-12 (이현직, 유영결, 유지호. 2010. 객체기반분류법을 이용한 토지피복정보의 정확도 향상 방안. 한국지형공간정보학회지 18 (3):11-12).
  14. Lee, J.B., J. Heo, and Y.D. Eo, 2007. Study on selection of optimized segmentation parameters and analysis of classification accuracy for object-oriented classification. Korean Journal of Remote Sensing 23 (6):521-528 (이정빈, 허준, 어양담. 2007. 객체기반 영상분류에서 최적 가중치 선정과 정확도 분석 연구. 대한원격탐사학회지 23(6): 521-528). https://doi.org/10.7780/kjrs.2007.23.6.521
  15. Lien T.H. B. Lars, and A. Salman. 2016. Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach. International Journal of Applied Earth Observation and Geoinformation 50:187-197. https://doi.org/10.1016/j.jag.2016.03.015
  16. Lim, O.T. 2016. A study on identifying individual Korean fir(Abies koreana) trees at Mt. Halla, Jeju, Korea, using UAV(Unmanned Aerial Vehicle) images and the object-based image analysis method. Master's thesis, Kookmin University. Korea.(임원택. 2016. UAV(unmanned aerial vehicle) 영상과 객체기반영상분석(object-based image analysis) 방법을 활용한 한라산 구상나무(Abies koreana) 개체 식별에 관한 연구. 국민대학교 석사학위 논문.
  17. Lowe, D. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision. pp.1-28.
  18. Na, H. S., J.M. Park, and J.S Lee. 2015. Analysis of land cover classification and pattern using remote sensing and spatial statistical method(focusing on the DMZ region in Gangwon-Do). Journal of the Korean Association of Geographic Information Studies 18(4):100-118 (나현섭, 박정묵, 이정수. 2015. 원격탐사와 공간통계 기법을 이용한 토지피복 분류 및 패턴 분석(강원도 DMZ일원을 대상으로). 한국지리정보학회지 18(4):100-118). https://doi.org/10.11108/kagis.2015.18.4.100
  19. Na, H.S. and J.S Lee. 2014. Analysis of land cover characteristics with object -based classification method -focusing on the DMZ in Inje-gun, Gangwon-do. Journal of the Korean Association of Geographic Information Studies 17(2): 121-135 (나현섭, 이정수. 2014. 객체기반 분류기법을 이용한 토지피복 특성분석-강원도 인제군의 DMZ지역 일원을 대상으로. 한국지리정보학회지 17(2):121-135. https://doi.org/10.11108/kagis.2014.17.2.121
  20. Park, J.J., C.Y. Ku, and B.S. Kim. 2007. Improvement of the level-2 land cover map with satellite image. Journal of GIS Association of Korea 15(1):67-80 (박정재, 구자용, 김병선, 2007. 위성영상을 이용한 중분류 토지피복도의 제작과정 개선. 한국공간정보학회지 15(1):67-80).
  21. Remondino, F., L. Barazzetti, F. Nex, M. Scaioniand, and D. Sarazzi. 2011. UAV photogrammetry for mapping and 3d modeling-current status and future perspectives. International Archives of the Photogrammetry, Remote Sensing and Spatial information Sciences 38(1):C22.
  22. Shin, J.S, T.H. Lee, P.M. Jung, and H.S. Kwon. 2015. A study on land cover map of UAV imagery using an object-based classification method. Jorunal of the Korean Society for Geospatial Information Science 23(4):25-33 (신지선, 이태호, 정필모, 권혁수. 2015. 객체기반 분류기법을 이용한 UAV 영상의 토지피복도 제작 연구. 한국지형공간정보학회지 23(4):25-33). https://doi.org/10.7319/kogsis.2015.23.4.025
  23. Sung, S.M. and J.O. Lee. 2016. Accuracy of parcel boundary demarcation in agricultural area using UAV-photogrammetry. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography 34(1):53-62 (성상민, 이재원. 2016. 무인항공사진측량에 의한 농경지 필지 경계설정 정확도. 한국측량학회지 34(1):53-62). https://doi.org/10.7848/ksgpc.2016.34.1.53
  24. Yoon, Y.S., Y.S. Kim, and Y.S. Kim. 2007. A study on the object-based classification method for wildfire fuel type map. Korea Aerospace Research Institute 6(1):213-221 (윤여상, 김윤수, 김용승. 2007. 산불연료지도 제작을 위한 객체기반 분류 방법 연구. 항공우주기술 6(1):213-221).
  25. Youngsinn, S. and N.S. Rebello. 2000. Supervised and unsupervised spectral angle classifiers. Photogrammetric Engineering and Remote Sensing 68(12): 1271-1280.

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  2. UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia) vol.13, pp.16, 2021, https://doi.org/10.3390/rs13163272