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

다국어 초록 (Multilingual Abstract)

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.

국문 초록 (Abstract)

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.

참고문헌 (Reference)

1 최한승, "선형정보의 연관분석을 통한 드론영상의 영상등록" 한국측량학회 35 (35): 441-465, 2017

2 나상일, "무인기 기반 RGB 영상을 이용한 동계작물 바이오매스 평가 모델 개발" 대한원격탐사학회 34 (34): 709-720, 2018

3 Mulla, D. J., "Twenty five years of remote sensing in precision agriculture : Key advances and remaining knowledge gaps" 114 (114): 358-371, 2013

4 Rokhmana, C. A., "The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia" 24 : 245-253, 2015

5 Tokekar, P., "Sensor planning for a symbiotic UAV and UGV system for precision agriculture" 32 (32): 1498-1511, 2016

6 김태헌, "SURF 기법과 상호정보기법을 활용한 농경지 지역 무인항공기 영상 간 정밀영상등록" 대한원격탐사학회 35 (35): 945-957, 2019

7 Honkavaara, E., "Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture" 5 (5): 5006-5039, 2013

8 Maes, W. H., "Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture" 24 (24): 152-164, 2019

9 Torres-Sanchez, J., "Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV" 103 : 104-113, 2014

10 Kim, D. W., "Modeling and testing of growth status for Chinese cabbage and white radish with UAV-based RGB imagery" 10 (10): 563-, 2018

11 Xiang, H., "Method for automatic georeferencing aerial remote sensing(RS)images from an unmanned aerial vehicle(UAV)platform" 108 (108): 104-113, 2011

12 Nongsaro, "Information of crops varieties" Rural Development Administration

13 Torres-Sanchez, J., "High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology" 10 (10): 2015

14 Uysal, M., "DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler hill" 73 : 539-543, 2015

15 Yeom, J., "Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture" 11 (11): 1548-, 2019

16 Woebbecke, D. M., "Color indices for weed identification under various soil, residue, and lighting conditions" 38 (38): 259-269, 1995

17 Diaz-Varela, R. A., "Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle" 134 : 117-126, 2014

18 Han, Y., "Automated coregistration of multisensor orthophotos generated from unmanned aerial vehicle platforms" 1 (1): 431-435, 2019

19 Tsai, C. H., "An accelerated image matching technique for UAV orthoimage registration" 128 : 130-145, 2017

20 Otsu, N., "A threshold selection method from graylevel histograms" 9 (9): 62-66, 1979

21 Wei, Z., "A small UAV based multi-temporal image registration for dynamic agricultural terrace monitoring" 9 (9): 904-, 2017

22 Wang, C., "A simplified empirical line method of radiometric calibration for small unmanned aircraft systems-based remote sensing" 8 (8): 1876-1885, 2015