한국측량학회지 (Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)

  • 제37권6호
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  • Pages.499-506
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  • 2019
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  • 1598-4850(pISSN)
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  • 2288-260X(eISSN)
한국측량학회 (Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)
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무인항공기 데이터의 영역 확장법 적용을 통한 정규수치표면모델 추출 및 경사도 파라미터 설정

Normalized Digital Surface Model Extraction and Slope Parameter Determination through Region Growing of UAV Data

  • Yeom, Junho (Dept. of Civil Engineering, Gyeongsang National University) ;
  • Lee, Wonhee (School of Convergence & Fusion System Engineering, Kyungpook National University) ;
  • Kim, Taeheon (Dept. of Geospatial Information, Kyungpook National University) ;
  • Han, Youkyung (School of Convergence & Fusion System Engineering, Kyungpook National University)
  • 투고 : 2019.11.20
  • 심사 : 2019.12.27
  • 발행 : 2019.12.31
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초록

정규수치표면모델(NDSM: Normalized Digital Surface Model)은 원격탐사데이터의 상세 분석을 위한 핵심 적인 자료로 사용된다. 지상기준높이인 정규수치표면모델을 생성하기 위한 가장 간단한 방법은 수치표면모델(DSM: Digital Surface Model)에서 수치지형모델(DTM: Digital Terrain Model)을 차분하는 것이지만, 무인항공기 데이터의 경우 높은 해상도의 특성상 식생, 도심 구조물 등 많은 수의 복잡한 지형지물을 포함하고 있어 정확한 수치지형모델을 추출하기 어렵다. 본 연구에서는 무인항공기 데이터의 고해상도 특성을 잘 살리고 비용효율적인 수치지형모델 생성이 가능하도록 RGB 기반 식생 지수인 ExG (Excess Green)를 이용하여 낮은 ExG 값을 갖는 영역 확장법의 초기 시드점을 선정하였다. 이때 국소적으로 낮은 식생지수 값을 갖는 초기 시드점이 잘못 추출되는 문제를 해결하기 위하여 지역적 윈도우 분석을 적용하였다. 이후, 해당 위치의 수치표면모델값을 바탕으로 영역 확장법을 적용하여 이웃하는 지면 화소들을 병합하였다. 영역 확장법 적용을 위해 경사도 파라미터가 사용되었으며 최종적으로 병합된 세그먼트의 크기가 0.25㎡ 초과일 경우 초기 시드점을 지면점으로 결정하였다. 다양한 경사도 파라미터 값을 설정하여 무인항공기 데이터 기반 정규수치표면모델 생성의 최적 경사도 기준값을 도출하고자 하였다. 최종적으로 추출된 지면점들에 대한 정확도 평가를 수행하였으며 지면점들에 보간법을 적용하여 정규수치표면모델을 생성하고 제안 기법을 농업지역에 적용하여 농작물의 지상기준높이 추출 및 농업 모니터링 가능성을 검증하였다.

NDSM (Normalized Digital Surface Model) is key information for the detailed analysis of remote sensing data. Although NDSM can be simply obtained by subtracting a DTM (Digital Terrain Model) from a DSM (Digital Surface Model), in case of UAV (Unmanned Aerial Vehicle) data, it is difficult to get an accurate DTM due to high resolution characteristics of UAV data containing a large number of complex objects on the ground such as vegetation and urban structures. In this study, RGB-based UAV vegetation index, ExG (Excess Green) was used to extract initial seed points having low ExG values for region growing such that a DTM can be generated cost-effectively based on high resolution UAV data. For this process, local window analysis was applied to resolve the problem of erroneous seed point extraction from local low ExG points. Using the DSM values of seed points, region growing was applied to merge neighboring terrain pixels. Slope criteria were adopted for the region growing process and the seed points were determined as terrain points in case the size of segments is larger than 0.25 ㎡. Various slope criteria were tested to derive the optimized value for UAV data-based NDSM generation. Finally, the extracted terrain points were evaluated and interpolation was performed using the terrain points to generate an NDSM. The proposed method was applied to agricultural area in order to extract the above ground heights of crops and check feasibility of agricultural monitoring.

키워드

참고문헌

  1. Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M.L., and Bareth, G. (2015), Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley, International Journal of Applied Earth Observation and Geoinformation, Vol. 39, pp. 79-87. https://doi.org/10.1016/j.jag.2015.02.012
  2. Bigdeli, B., Amirkolaee, H.A., and Pahlavani, P. (2018), DTM extraction under forest canopy using LiDAR data and a modified invasive weed optimization algorithm, Remote sensing of environment, Vol. 216, pp. 289-300. https://doi.org/10.1016/j.rse.2018.06.045
  3. Chen, Z., Xu, B., and Gao, B. (2016), An image-segmentationbased urban DTM generation method using airborne LiDAR data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, No. 1, pp. 496-506. https://doi.org/10.1109/JSTARS.2015.2512498
  4. Im, Y. and Kim, T. (2002), Comparison of DEM accuracy and quality over urban area from SPOT, EOC and IKONOS stereo pairs, Korean Journal of Remote Sensing, Vol. 18, No. 4, pp. 221-231. (in Korean with English abstract)
  5. Lee, G., Choi, Y., Jung, K., and Cho, G. (2015), Analysis of the spatial information accuracy according to photographing direction of fixed wing UAV, Journal of the Korean Cadastre Information Association, Vol. 17, No. 3, pp. 141-149. (in Korean with English abstract)
  6. Lee, K. and Lee, W. (2016), Orthophoto and DEM generation using low specification UAV images from different altitudes, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 34, No. 5, pp. 535-544. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2016.34.5.535
  7. Li, S., Tang, H., He, S., Shu, Y., Mao, T., Li, J., and Xu, Z. (2015), Unsupervised detection of earthquake-triggered roof-holes from UAV images using joint color and shape features, IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 9, pp. 1823-1827. https://doi.org/10.1109/LGRS.2015.2429894
  8. Otsu, N. (1975), A threshold selection method from gray-level histograms, Automatica, Vol. 11, pp. 285-296. https://doi.org/10.1016/0005-1098(75)90044-8
  9. Park, C. and Lee, G. (2018), Study on production of DEM using aerial photo, Journal of the Korean Geomorphological Association, Vol. 25, No. 3, pp. 105-120. (in Korean with English abstract) https://doi.org/10.16968/JKGA.25.3.105
  10. Park, S. and Bae, Y. (2004), The optimal grid resolution to interpret the spatial structure of geomorphological processes over the landscape, Journal of the Korean Geomorphological Association, Vol. 11, No. 3, pp. 113-136. (in Korean with English abstract)
  11. Rhee, S., Jeong, J., Lee, T., and Kim, T. (2011), DEM generation and accuracy comparison from multiple Kompsat-2 images, Korean Journal of Remote Sensing, Vol. 27, No. 1, pp. 51-58. (in Korean with English abstract) https://doi.org/10.7780/kjrs.2011.27.1.051
  12. Torres-Sanchez, J., Pena-Barragan, J.M., De Castro, A.I., and Lopez-Granados, F. (2014), Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV, Computers and Electronics in Agriculture, Vol. 103, pp. 104-113. https://doi.org/10.1016/j.compag.2014.02.009
  13. Woebbecke, D.M., Meyer, G.E., Von Bargen, K., and Mortensen, D.A. (1995), Color indices for weed identification under various soil, residue, and lighting conditions, Transactions of the American Society of Agricultural Engineers, Vol. 38, No. 1, pp. 259-269. https://doi.org/10.13031/2013.27838
  14. Xu, C., Lu, Z., Xu, G., Feng, Z., Tan, H., and Zhang, H. (2015), 3D Reconstruction of tree-crown based on the UAV aerial images, Mathematical Problems in Engineering, Vol. 2015, 318619.
  15. Yeom, J., Jung, J., Chang, A., Ashapure, A., Maeda, M., Maeda, A., and Landivar, J. (2019), Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture, Remote Sensing, Vol. 11, No. 13, pp. 1548. https://doi.org/10.3390/rs11131548
  16. Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G. (2016), An easy-to-use airborne LiDAR data filtering method based on cloth simulation, Remote Sensing, Vol. 8, No. 6, 501. https://doi.org/10.3390/rs8060501