대한공간정보학회지 (Journal of Korean Society for Geospatial Information Science)

  • 제22권3호
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  • Pages.155-160
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  • 2014
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  • 1598-2955(pISSN)
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  • 2287-6693(eISSN)
대한공간정보학회 (Korea Spatial Information Society)
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해상도변화에 따른 항공초분광영상 토지피복분류의 분류정확도 비교 연구

Study of Comparison of Classification Accuracy of Airborne Hyperspectral Image Land Cover Classification though Resolution Change

  • 조형갑 ((주)지오스토리, 공간정보팀) ;
  • 김동욱 ((주)지오스토리, 솔루션개발팀) ;
  • 신정일 ((주)지오스토리, 기술연구소)
  • 투고 : 2014.09.11
  • 심사 : 2014.09.22
  • 발행 : 2014.09.30
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 각기 다른 3가지 해상도로 촬영된 항공 초분광영상을 이용하여 건물, 도로, 산림 등 8가지 분류군에 대해 토지피복분류를 실시하고 정확도를 비교하는 연구를 수행하였다. 연구는 24밴드(0.5m 공간해상도), 48밴드(1.0m 공간해상도), 96밴드(1.5m 공간해상도)로 각각 1000m, 2000m, 3000m고도에서 촬영된 초분광영상을 이용하여 8가지 클래스에 대해 토지피복분류를 수행하였다. 그 결과 2000m고도에서 촬영된 48밴드 초분광영상을 이용하여 분류한 영상이 가장 높은 분류정확도를 보였고, 24밴드, 96밴드 순으로 분류정확도가 높게 나타났다. 초분광영상 활용에 있어서 1m 공간해상도에 48개밴드를 사용하여 토지피복분류를 수행함에 있어 적합함을 확인하였고 항공 초분광영상을 활용한 주제도 제작과 관련하여 정확도와 실용성 면에서 공간정보 품질이 개선될 것으로 기대한다.

This paper deals with comparison of classification accuracy between three land cover classification results having difference in resolution and they were classified with eight classes including building, road, forest, etc. Airborne hyperspectral image used in this study was acquired at 1000m, 2000m, 3000m elevation and had 24 bands(0.5m spatial resolution), 48 bands(1.0m), 96 bands(1.5m). Assessment of classification accuracy showed that the classification using 48 bands hyperspectral image had outstanding result as compared with other images. For using hyperspectral image, it was verified that 1m spatial resolution image having 48 bands was appropriate to classify land cover and qualitative improvement is expected in thematic map creation using airborne hyperspectral image.

키워드

참고문헌

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  1. Vicarious Radiometric Calibration of RapidEye Satellite Image Using CASI Hyperspectral Data vol.23, pp.3, 2015, https://doi.org/10.7319/kogsis.2015.23.3.003
  2. Accuracy Assessment of Supervised Classification using Training Samples Acquired by a Field Spectroradiometer: A Case Study for Kumnam-myun, Sejong City vol.24, pp.1, 2016, https://doi.org/10.7319/kogsis.2016.24.1.121
  3. 연안 해저 피복 분류를 위한 항공 초분광영상의 수심보정 vol.23, pp.2, 2014, https://doi.org/10.12672/ksis.2015.23.2.031
  4. 연안 해저 재질 분석을 위한 초분광영상의 보정 방법 vol.19, pp.2, 2014, https://doi.org/10.11108/kagis.2016.19.2.107
  5. 하이퍼센서 정보를 이용한 태화강지역의 비점오염원 분석 vol.20, pp.1, 2014, https://doi.org/10.11108/kagis.2017.20.1.056