[논문]농경지 지역 무인항공기 영상 기반 시계열 수치표고모델 표고 보정

김태헌; 박주언; 윤예린; 이원희; 한유경

doi:10.7848/ksgpc.2020.38.3.223

농경지 지역 무인항공기 영상 기반 시계열 수치표고모델 표고 보정
Elevation Correction of Multi-Temporal Digital Elevation Model based on Unmanned Aerial Vehicle Images over Agricultural Area 원문보기

한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.38 no.3, 2020년, pp.223 - 235

김태헌 (Dept. of Geospatial Information, Kyungpook National University) , 박주언 (School of Convergence & Fusion System Engineering, Kyungpook National University) , 윤예린 (School of Convergence & Fusion System Engineering, Kyungpook National University) , 이원희 (School of Convergence & Fusion System Engineering, Kyungpook National University) , 한유경 (School of Convergence & Fusion System Engineering, Kyungpook National University)

초록
AI-Helper

본 연구에서는 무인항공기 영상 기반의 정밀농업(precision agricultural) 구현에 있어 핵심 데이터 중 하나인 수치표고모델의 표고를 보정하기 위한 수치표고모델 표고 보정 방법론을 제시한다. 먼저 정사영상에 방사보정을 수행한 다음 ExG (Excess Green)를 생성한다. ExG에 Otsu 기법을 적용하여 산출된 임계값을 기준으로 비식생지역을 추출한다. 이어서, 비식생지역의 위치에 대응되는 수치표고모델의 표고를 표고 보정을 위한 데이터인 EIFs(Elevation Invariant Features)로 추출한다. 추출된 EIFs 간 차이값을 기반으로 정규화된 Z-score를 산출하여 포함된 특이치를 제거한다. 그리고 선형회귀식을 구성하여 수치표고모델의 표고를 보정함으로써 지상기준점 데이터 없이 고품질의 수치표고모델을 제작한다. 총 10장의 수치표고모델을 활용하여 제안기법을 검증하기 위해 표고 보정 전과 후의 최대/최소값, 평균/표준편차를 비교분석하였다. 또한, 검사점을 선정하여 RMSE (Root Mean Square Error)를 산출한 결과, 정확도는 평균 RMSE 0.35m로 도출되었다. 이를 통해 지상기준점 데이터 없이 고품질의 수치표고모델을 제작할 수 있음을 확인하였다.

Abstract ▼ AI-Helper

In this study, we propose an approach for calibrating the elevation of a DEM (Digital Elevation Model), one of the key data in realizing unmanned aerial vehicle image-based precision agriculture. First of all, radiometric correction is performed on the orthophoto, and then ExG (Excess Green) is generated. The non-vegetation area is extracted based on the threshold value estimated by applying the Otsu method to ExG. Subsequently, the elevation of the DEM corresponding to the location of the non-vegetation area is extracted as EIFs (Elevation Invariant Features), which is data for elevation correction. The normalized Z-score is estimated based on the difference between the extracted EIFs to eliminate the outliers. Then, by constructing a linear regression model and correcting the elevation of the DEM, high-quality DEM is produced without GCPs (Ground Control Points). To verify the proposed method using a total of 10 DEMs, the maximum/minimum value, average/standard deviation before and after elevation correction were compared and analyzed. In addition, as a result of estimating the RMSE (Root Mean Square Error) by selecting the checkpoints, an average RMSE was derivsed as 0.35m. Comprehensively, it was confirmed that a high-quality DEM could be produced without GCPs.

주제어

표/그림 (21)

그림 Fig. 1. Methodology of proposed DEM elevation correction
그림 Fig. 2. Reﬂectance panels for radiometric correction: (a) Reﬂectance panels, (b) Speciﬁcation of reﬂectance panels
그림 Fig. 3. Experimental site: Gyeongsangbuk-do, Sangju city, South Korea
표 Table 1. Speciﬁcation of the UAV and sensor
표 Table 2. Speciﬁcation of multi-temporal orthophoto and DEM
그림 Fig. 4. Location of checkpoints
그림 Fig. 5. Linear regression model for performing radiometric correction of the RGB orthophotos in blue band acquired reference orthophoto
표 Table 3. Accuracy of reference image and DEM (unit: cm)
표 Table 4. Reﬂectance values of reﬂectance panels
그림 Fig. 6. Results of radiometric correction: (a) Raw reference orthophoto, (b) Raw subject orthophoto, (c) Radiometric corrected reference orthophoto, (d) Radiometric corrected subject orthophoto
그림 Fig. 7. Spectral pattern for the RGB orthophotos in the red band: (a) Raw reference orthophoto, (b) Raw subject orthophoto, (c) Radiometric corrected reference orthophoto, (d) Radiometric corrected subject orthophoto
그림 Fig. 8. The ExG generated based on the radiometric corrected orthophoto: (a) Reference orthophoto, (b) Subject orthophoto
그림 Fig. 9. Results of ExG binarization: (a) Reference orthophoto, (b) Subject orthophoto
그림 Fig. 10. Results of EIFs extraction: (a) Reference orthophoto, (b) Subject orthophoto, (c) Reference DEM, (d) Subject DEM
그림 Fig. 11. Normalized Z-score and threshold value for eliminating outliers
그림 Fig. 12. Results of outliers elimination: (a) Reference orthophoto, (b) Subject orthophoto, (c) Reference DEM, (d) Subject DEM
그림 Fig. 13. Linear regression model for performing DEM correction (Reference DEM: 2019-8-1, Subject DEM: 2019-6-21)
그림 Fig. 14. Results of outliers elimination: (a) Reference DEM, (b) Subject DEM, (c) Corrected subject DEM
표 Table 5. Correlation coeﬃcient of linear regression model
표 Table 6. Min/max, mean, and standard deviation values of DEM correction results
표 Table 7. The RMSE of DEM correction results (unit: m)

참고문헌 (22)

Choi, H.S. and Kim, E.M. (2017), Image registration of drone images through association analysis of linear features, Journal of Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 35, No. 6, pp. 441-452. (in Korean with English abstract)
Diaz-Varela, R.A., Zarco-Tejada, P.J., Angileri, V., and Loudjani, P. (2014), Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle, Journal of Environmental Management, Vol. 134, pp. 117-126.

상세보기
Han, Y., Choi, J., Jung, J., Chang, A., Oh, S., and Yeom, J. (2019), Automated coregistration of multisensor orthophotos generated from unmanned aerial vehicle platforms, Journal of Sensors, Vol. 1, No. 91, pp. 431-435.
Honkavaara, E., Saari, H., Kaivosoja, J., Polonen, I., Hakala, T., Litkey, P., and Pesonen, L. (2013), Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture, Remote Sensing, Vol. 5, No. 10, pp. 5006-5039.

상세보기
Kim, D.W., Yun, H.S., Jeong, S.J., Kwon, Y.S., Kim, S.G., Lee, W.S., and Kim, H.J. (2018), Modeling and testing of growth status for Chinese cabbage and white radish with UAV-based RGB imagery, Remote Sensing, Vol. 10, No. 4, pp. 563.

상세보기
Kim, T.H., Lee, K.L., Lee, W.H., Yeom, J.H., Jung, S.J., and Han, Y.K. (2019), Coarse to fine image registration of unmanned aerial vehicle images over agricultural area using SURF and mutual information methods, Korean Journal of Remote Sensing, Vol. 35, No. 6-1, pp. 945-957. (in Korean with English abstract)
Maes, W.H. and Steppe, K. (2019), Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture, Trends in Plant Science, Vol. 24, No. 2, pp. 152-164.

상세보기
Mulla, D.J. (2013), Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps, Biosystems Engineering, Vol. 114, No. 4, pp. 358-371.

상세보기
Na, S.I., Park, C.W., So, K.H., Ahn, H.Y., and Lee, K.D. (2018), Development of biomass evaluation model of winter crop using RGB imagery based on unmanned aerial vehicle, Korean Journal of Remote Sensing, Vol. 34, No. 5, pp. 709-720. (in Korean with English abstract)
Nongsaro. (2020), Information of crops varieties, Rural Development Administration, Republic of Korea, http://www.nongsaro.go.kr (last date accessed: 27 May 2020)
Otsu, N. (1979), A threshold selection method from graylevel histograms, IEEE transactions on systems, man, and cybernetics, Vol. 9, No. 1, pp. 62-66.

상세보기
Rokhmana, C.A. (2015), The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia, Procedia Environmental Sciences, Vol. 24, pp. 245-253.

상세보기
Tokekar, P., Vander Hook, J., Mulla, D., and Isler, V. (2016), Sensor planning for a symbiotic UAV and UGV system for precision agriculture, IEEE Transactions on Robotics, Vol. 32, No. 6, pp. 1498-1511.

상세보기
Torres-Sanchez, J., Lopez-Granados, F., Serrano, N., Arquero, O., and Pena, J.M. (2015), High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology, PloS one, Vol. 10 No. 6.

상세보기
Torres-Sanchez, J., Pena, 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.

상세보기
Tsai, C.H. and Lin, Y.C. (2017), An accelerated image matching technique for UAV orthoimage registration, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 128, pp. 130-145.

상세보기
Uysal, M., Toprak, A.S., and Polat, N. (2015), DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler hill, Measurement, Vol. 73, pp. 539-543.
Wang, C. and Myint, S.W. (2015), A simplified empirical line method of radiometric calibration for small unmanned aircraft systems-based remote sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, No. 5, pp. 1876-1885.

상세보기
Wei, Z., Han, Y., Li, M., Yang, K., Yang, Y., Luo, Y., and Ong, S.H. (2017), A small UAV based multi-temporal image registration for dynamic agricultural terrace monitoring, Remote Sensing, Vol. 9, No. 9, pp. 904.

상세보기
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 ASAE, Vol. 38, No. 1, pp. 259-269.

상세보기
Xiang, H. and Tian, L. (2011), Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform, Biosystems Engineering, Vol. 108, No. 2, pp. 104-113.

상세보기
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.

저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증