• Smart Media Journal
• /
• v.9 no.3
• /
• pp.46-51
• /
• 2020

Pine wilt disease is one of the reasons that results in huge damage on pine trees in east Asia including Korea, Japan, and China, and early finding and removing the diseased trees is an efficient way to prevent the forest from wide spreading. This paper proposes a searching method of the damaged pine trees from wilt disease in ortho-images corrected from RGB images, which are captured by unmanned aviation vehicles. The proposed method constructs patch-based classifier using ResNet18 backbone network, classifies the RGB ortho-image patches, and make the results as a heat map. The heat map can be used to find the distribution of diseased pine trees, to show the trend of spreading disease, and to extract the RGB distribution of the diseased areas in the image. The classifier in the work shows 94.7% of accuracy.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.25 no.4
• /
• pp.136-150
• /
• 2022

In this study, a comparative review was conducted on how to use RGB images to obtain river topographic information, which is one of the most essential data for eco-friendly river management and flood level analysis. In terms of the topographic information of river zone, to obtain the topographic information of flow section is one of the difficult topic, therefore, this study focused on estimating the river topographic information of flow section through RGB images. For this study, the river topography surveying was directly conducted using ADCP and RTK-GPS, and at the same time, and orthomosiac image were created using high-resolution images obtained by drone photography. And then, the existing developed regression equations were applied to the result of channel topography surveying by ADCP and the band values of the RGB images, and the channel bathymetry in the study area was estimated using the regression equation that showed the best predictability. In addition, CCHE2D flow modeling was simulated to perform comparative verification of the topographical informations. The modeling result with the image-based topographical information provided better water depth and current velocity simulation results, when it compared to the directly measured topographical information for which measurement of the sub-section was not performed. It is concluded that river topographic information could be obtained from RGB images, and if additional research was conducted, it could be used as a method of obtaining efficient river topographic information for river management.

• Journal of the Society of Disaster Information
• /
• v.14 no.4
• /
• pp.501-509
• /
• 2018

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.

• Journal of Urban Science
• /
• v.6 no.2
• /
• pp.17-27
• /
• 2017

Recently, civil complaints have increased due to water pollution and bad smell in rivers. Therefore, attention is focused on improving the river environment. The purpose of this study is to acquire RGB and thermal infrared images using UAV for sewage outlet and to monitor the status of stream pollution and the applicability UAV based images for river embankment maintenance plan was examined. The accuracy of the 3D model was examination by SfM(Structure from Motion) based images analysis on river embankment maintenance area. Especially, The wastewater discharged from the factory near the river was detected as an thermal infrared images and the flow of wastewater was monitored. As a result of the study, we could monitor the cause and flows of wastewater pollution by detecting temperature change caused by wastewater inflow using UAV images. In addition, UAV based a high precision 3D model (DTM, Digital Topographic Map, Orthophoto Mosaic) was produced to obtain precise DSM(Digital Surface Model) and vegetation cover information for river embankment maintenance.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.3
• /
• pp.83-98
• /
• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.5
• /
• pp.1645-1653
• /
• 2014

In general, RGB, NIR, and thermal images are used for obtaining geospatial data. Such multiband images are collected via devices mounted on satellites or manned flights, but do not always meet users' expectations, due to issues associated with temporal resolution, costs, spatial resolution, and effects of clouds. We believe high-resolution, multiband images can be obtained at desired time points and intervals, by developing a payload suitable for a low-altitude, auto-piloted UAV. To achieve this, this study first established a low-cost, high-resolution multiband image collection system through developing a sensor and a payload, and collected geo-referencing data, as well as RGB, NIR and thermal images by using the system. We were able to obtain a 0.181m horizontal deviation and 0.203m vertical deviation, after analyzing the positional accuracy of points based on ortho mosaic images using the collected RGB images. Since this meets the required level of spatial accuracy that allows production of maps at a scale of 1:1,000~5,000 and also remote sensing over small areas, we successfully validated that the payload was highly utilizable.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.346-346
• /
• 2020

홍수기 효과적인 하천관리를 위해서는 광역 모니터링을 위한 기술 확보가 매우 중요하며, 최근 드론을 활용한 하천 모니터링에 관한 관심이 점차 증가되고 있다. 하천관리에 필요한 드론 탑재용 센서는 기본적으로 RGB 광학센서를 비롯하여 근적외선(Nir) 및 열적외선 센서가 함께 운용되는 것이 효과적이다. 그러나 현재 판매되는 드론 카메라를 살펴보면 근적외선과 열적외선 센서가 별도로 분리되어 있고 광학센서에 비해 상대적으로 매우 고가로 판매되고 있는 실정이다. 따라서 하천 모니터링을 위해서는 광학(RGB), 근적외선 그리고 열적외선 센서가 통합된 저가의 탑재체 개발이 시급하고 이를 활용한 하천 모니터링 프로세스를 정립할 필요가 있다. 본 연구에서는 일반 드론에 쉽게 탑재 가능한 하천 모니터링용 탑재체를 개발하였으며, 이를 기반으로 하천 홍수 및 부유사 모니터링에 활용하였다. 광학센서는 하천의 주요 형상을 확인하는데 이용하였으며, 근적외선 센서는 홍수 및 부유사 탐지에 활용하였다. 특히 본 연구에서는 비교적 넓은 하천 구역에 대한 공간정보를 구축하기 위해 75% 이상의 중복도를 가지고 촬영하도록 세팅하였으며 영상접합 SW를 활용하여 정사영상을 생성하였다. 구축한 근적외선 정사영상으로부터 영상분석 프로그램을 활용하여 홍수 및 부유사 영역을 추출하였으며 이를 통해 홍수기 하천 모니터링 및 치수 업무 의사결정을 위한 정보를 제공할 수 있었다. 저가용 드론 센서는 상용 SW와의 연계가 어렵기 때문에 자동비행 프로그램처럼 해당 위치별 영상 촬영이 어려운 한계가 있었으며, 본 연구에서는 센서의 제원특성을 활용하여 자동비행 SW에서도 일정 이상의 중복도를 확보할 수 있는 비행고도별 촬영시간 등을 종합적으로 설계하였다. 이를 통해 해당 지역에 대한 하천 모니터링용 정사영상을 구축할 수 있었으며 기존의 고가용 드론 센서와 유사한 효과를 가져올 수 있었다.

• Proceedings of the Korea Contents Association Conference
• /
• 2005.05a
• /
• pp.138-141
• /
• 2005

본 연구에서는 위성영상으로부터 정사투영 영상을 제작하고, 지상의 기준점 측량을 GCP를 이용하여 실시하여 경도, 위도 고도의 참조좌표를 정확히 수집하였다. 1:5,000 지형도를 디지타이징하여 만들어진 등고선도를 DEM으로 변환하여 고도별 RGB영상으로 화면에 보여지도록 하고, 각각의 경우에 대하여 제작된 정사투영 영상에 중첩해 봄으로써 제작된 정사투영영상의 정확도를 점검하여 수치지형도를 대신할 수 있는 3차원 영상지도를 제작하였다. 대상지역의 입체지형분석을 위한 3차원 입체 영상지도를 제작과 더불어 DEM을 이용한 지형의 경사도 분석과 방향분석, 지형표고모델, 다방향 입체영상을 생성할 수 있도록 하였다. 장차 국토계획 및 건설분야에서의 지형분석과 각종 구조물의 배치 및 관리, 하천 수계의 분포에 대한 댐 건설 최적지 선정, 도로계획선에 따른 각 방향의 조감도 제작, 토지 피복분류에 의한 토지이용과 지역개발계획 등 지역환경을 종합적으로 진단해 볼 수 있는 활용방안을 도출할 수 있는 적용실험을 하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.6
• /
• pp.589-599
• /
• 2018

The land cover map has been produced by using satellite and aerial images. However, these two images have the limitations in spatial resolution, and it is difficult to acquire images of a area at desired time because of the influence of clouds. In addition, it is costly and time-consuming that mapping land cover map of a small area used by satellite and aerial images. This study used multispectral camera-based drone to acquire multi-temporal images for orthoimages generation. The efficiency of produced land cover map was evaluated using time series analysis. The results indicated that the proposed method can generated RGB orthoimage and multispectral orthoimage with RMSE (Root Mean Square Error) of ${\pm}10mm$, ${\pm}11mm$, ${\pm}26mm$ and ${\pm}28mm$, ${\pm}27mm$, ${\pm}47mm$ on X, Y, H respectively. The accuracy of the pixel-based and object-based land cover map was analyzed and the results showed that the accuracy and Kappa coefficient of object-based classification were higher than that of pixel-based classification, which were 93.75%, 92.42% on July, 92.50%, 91.20% on October, 92.92%, 91.77% on February, respectively. Moreover, the proposed method can accurately capture the quantitative area change of the object. In summary, the suggest study demonstrated the possibility and efficiency of using multispectral camera-based drone in production of land cover map.

• Korean Journal of Remote Sensing
• /
• v.30 no.2
• /
• pp.207-218
• /
• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.