Journal of Korean Society for Geospatial Information Science (대한공간정보학회지)

Korea Spatial Information Society (대한공간정보학회)
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A Study on Land Cover Map of UAV Imagery using an Object-based Classification Method

객체기반 분류기법을 이용한 UAV 영상의 토지피복도 제작 연구

  • Shin, Ji Sun (Ecosystem Services Research, Bureau of Ecological Conservation Research, National Institute of Ecology) ;
  • Lee, Tae Ho (Ecosystem Services Research, Bureau of Ecological Conservation Research, National Institute of Ecology) ;
  • Jung, Pil Mo (Ecosystem Services Research, Bureau of Ecological Conservation Research, National Institute of Ecology) ;
  • Kwon, Hyuk Soo (Ecosystem Services Research, Bureau of Ecological Conservation Research, National Institute of Ecology)
  • 신지선 (국립생태원 생태보전본부 생태계서비스연구부) ;
  • 이태호 (국립생태원 생태보전본부 생태계서비스연구부) ;
  • 정필모 (국립생태원 생태보전본부 생태계서비스연구부) ;
  • 권혁수 (국립생태원 생태보전본부 생태계서비스연구부)
  • Received : 2015.10.12
  • Accepted : 2015.11.27
  • Published : 2015.12.31
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Abstract

The study of ecosystem assessment(ES) is based on land cover information, and primarily it is performed at the global scale. However, these results as data for decision making have a limitation at the aspects of range and scale to solve the regional issue. Although the Ministry of Environment provides available land cover data at the regional scale, it is also restricted in use due to the intrinsic limitation of on screen digitizing method and temporal and spatial difference. This study of objective is to generate UAV land cover map. In order to classify the imagery, we have performed resampling at 5m resolution using UAV imagery. The results of object-based image segmentation showed that scale 20 and merge 34 were the optimum weight values for UAV imagery. In the case of RapidEye imagery;we found that the weight values;scale 30 and merge 30 were the most appropriate at the level of land cover classes for sub-category. We generated land cover imagery using example-based classification method and analyzed the accuracy using stratified random sampling. The results show that the overall accuracies of RapidEye and UAV classification imagery are each 90% and 91%.

생태계 평가 연구는 대부분 토지피복 정보를 기반으로 하여 연구되며, 주로 전지구적인 범위로 이루어져 왔다. 그러나 이러한 결과들을 지역적 현안에 대한 의사결정 자료로 활용하기에는 범위와 스케일에 있어서 활용성이 떨어지는 측면이 있다. 지역적 스케일에 활용 가능한 토지피복 정보로는 환경부에서 제작된 토지피복도가 있지만 시각판독법(On Screen Digitizing Method)의 한계와 시기별, 지역별 차이로 인해 자료 활용에 제한이 있다. 본 연구는 객체기반 분류기법을 이용하여 UAV 영상의 중분류 토지피복도를 제작하는데 목적이 있다. 이를 위하여 고해상도 UAV 영상을 5m 공간해상도로 재배열한 후 영상분할을 수행한 결과 scale 20, merge 34가 최적의 가중치 값으로 나타났으며, RapidEye 영상 분할에서는 scale 30, merge 30이 중분류 수준에 적절한 가중치 값으로 나타났다. 토지피복도는 예제기반분류를 사용하여 제작하였고, 층화추출법을 사용하여 정확도 검증을 수행하였다. 그 결과, RapidEye 분류 영상은 90%, UAV 분류 영상은 91%로 양호한 토지피복분류 결과가 도출되었다.

Keywords

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