• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.4
• /
• pp.223-233
• /
• 2018

In this study was classified the cadastral control points and parcel boundary points into 40m, 100m by flight altitude of UAV (Unmanned Aerial Vehicle) which compared the coordinates extracted from the orthophoto with the parcel boundary point coordinates by GNSS (Global Navigation Satellite System) ground survey. As a results of this study, first, in the spatial resolution analysis that the average error of the orthoimage by flight altitude were 0.024m at 40m, and 0.034m at 100m which were higher 40m than 100m for spatial resolution of orthophotos and position accuracy. Second, in order to analyze the accuracy of image recognition by airmark of flight altitude that was divided into three cases of nothing, green, and red of RMSE (Root Mean Square Error) were X=0.039m, Y=0.019m and Z=0.055m, the highest accuracy. Third, the result of the comparison between orthophotos and field survey results that showed the total RMSE error of the cadastral control points were X=0.029m, Y=0.028m, H=0.051m, and the parcel boundary points were X=0.041m, Y=0.030m. In conclusion, based on the results of this study, it is expected that if the average error of flight altitude is limited to less than 0.05m in the legal regulations related to orthophotos for cadastral surveying, it will be an economical and efficient method for cadastral survey as well as spatial information acquisition.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.4
• /
• pp.279-286
• /
• 2018

In the civil works, the measurement of earth-volume is one of the important elements in the estimation of the reasonable construction cost. Related studies mainly used GPS (Global Positioning System) or total station to obtain information on civil work areas. However, these methods are difficult to implement in inaccessible areas. Therefore, the aim of this paper is to use the UAV (Unmanned Aerial Vehicle) to measure the earth-volume. The study area is located in a reservoir construction site in Sangju-si, Gyeongsangbuk-do, Republic of Korea. We compared the earth-volume amounts acquired by UAV-based surveying to ones acquired by total station-based and GPS-based surveying, respectively. In the site, the amount of earth-volume acquired by GPS was $147,286.79m^3$. The amount of earth-volume acquired by total station was $147,286.79m^3$, which is the 96.13% accuracy compared to the GPS-based surveying. The earth-volume obtained by UAV was $143,997.05m^3$ when measured without GCPs (Ground Control Points), $147,251.71m^3$ with 4 GCPs measurement, and $146,963.81m^3$ with 7 GCPs measurement. Compared to the GPS-based surveying, 97.77%, 99.98%, and 99.78% accuracies were obtained from the UAV-based surveying without GCP, 4 GCPs, and 7 GCPs, respectively. Therefore, it can be confirmed that the UAV-based surveying can be used for the earth-volume measurement.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.36 no.1
• /
• pp.97-108
• /
• 2018

The purpose of this study is to propose the construction of a visibility analysis model, which is the basis of the analysis for landscape management on the heritage sites such as historic villages and scenic sites. Results of the visibility analysis using DEM and the visibility analysis of DSM based on 3D mapping data are compared as follows: Precision level of the extracted data was confirmed to be less than 6.5cm, based on RTK survey results produced by constructing orthoimage data and DSM from the digital data of 2cm-class GSD(Ground Sample Distance) obtained by using a small UAV(Unmanned Aerial Vehicle). As a result of comparing the visibility analysis data of Digital Surface Model (DSM) using a small UAV with Digital Elevation Model(DEM) applying the height of the building to the Digital Topographic Map, it was confirmed that more realistic visibility analysis can be accomplished by applying DSM, as the structures such as fences, trees, and houses are reflected in the topographic data. The visibility analysis model using the 3D mapping technique can efficiently obtain the constantly changing topographic information when needed, by immediately constructing the data by utilizing a small UAV. It seems to be possible to propose a reasonable analysis result for preservation management such as landscape evaluation of cultural property.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_2
• /
• pp.1083-1093
• /
• 2019

The purpose of this study is to detect the burned area change by direct burning of tree canopies and post-fire mortality of trees via analyzing satellite imageries from the Korea multi-purpose satellite-2 and -3 (KOMPSAT-2 and -3) for two large-fires over the Goseong-Sokcho and Gangneung-Donghae regions in April 2019. For each case, the burned area was compared between two dates: the day when the fire occurred and 15-18 days after it. As the results, within these two dates, there was no substantial difference in burned area of sites whose severities were marked as "Extreme", but sites with "High" and "Low" severities showed significant differences in burned area between the two dates. These differences were resulted from the lagged post-fire browning of canopies which was detected by images from in-situ observation,satellite, and the unmanned aerial vehicle. The post-fire browning started after 3-4 days and became apparent after 10-15 days. This study offers information about the timing to quantify the burned area by large fire and about the mechanism of post-fire mortality. Also, the findings can support policy makers in planning the restoration of the damaged areas.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.15 no.2
• /
• pp.111-118
• /
• 2016

This paper presents an X-band (9.375 GHz) $1{\times}2$ array microstrip antenna which is capable of active beam compensation for installation of an unmanned aerial vehicle (UAV). First of all, a basic $1{\times}2$ array microstrip antenna incorporated with wilkinson power divider was designed and performance of the array antenna was verified. Next, to verify beam steering performance of the designed array microstrip antenna, we fabricated a phase shifter, and the wilkinson power divider as module structure and measured characteristics of beam steering according to phase shifting. The main lobe is 0.6 dBi at $0^{\circ}$, and the side lobe decreased 18.8 dB. The reliable radiation pattern was achieved. Finally, an active beam steering microstrip array antenna attached with the phase shifter and the power divider on the back side of the antenna was designed and fabricated to install wing of UAV for compactness. The maximum gain is 0.1 dBi. Therefore, we obtained a basic antenna technology for compensating beam error according to wing deformation of an UAV installed array antennas.

• Korean Journal of Remote Sensing
• /
• v.35 no.5_1
• /
• pp.681-692
• /
• 2019

In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.

• Korean Journal of Remote Sensing
• /
• v.35 no.5_2
• /
• pp.819-829
• /
• 2019

In the disaster of collapse, an immediate response is needed to prevent the damage from worsening, and damage area calculation, response and recovery plan should be established. This requires accurate detection of the damage affected area. This study performed the detection of the damaged area by using UAV which can respond quickly and in real-time to detect the collapse accident. The study area was selected as B-05 housing redevelopment area in Jung-gu, Ulsan, where the demolition of houses and apartments in progress as the redevelopment project began. This area resembles a collapsed state of the building, which clear changes before and after the demolition. UAV images were acquired on May 17 and July 9, 2019, respectively. The changing area was considered as the damaged area before and after the collapse of the building, and the changing area was detected using CVA (Change Vector Analysis) the Representative Change Detection Technique, and SLIC (Simple Linear Iterative Clustering) based superpixel algorithm. In order to accurately perform the detection of the damaged area, the uninterested area (vegetation) was firstly removed using ExG (Excess Green), Among the objects that were detected by change, objects that had been falsely detected by area were finally removed by calculating the minimum area. As a result, the accuracy of the detection of damaged areas was 95.39%. In the future, it is expected to be used for various data such as response and recovery measures for collapse accidents and damage calculation.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1731-1738
• /
• 2021

The objective of this study was to extract individual trees and tree heights using UAV drone images. The study site was Gongju national university experiment forest, located in Yesan-gun, Chungcheongnam-do. The thinning intensity study sites consisted of 40% thinning, 20% thinning, 10% thinning and control. The image was filmed by using the "Mavic Pro 2" model of DJI company, and the altitude of the photo shoot was set at 80% of the overlay between 180m pictures. In order to prevent image distortion, a ground reference point was installed and the end lap and side lap were set to 80%. Tree heights were extracted using Digital Surface Model (DSM) and Digital Terrain Model (DTM), and individual trees were split and extracted using object-based analysis. As a result of individual tree extraction, thinning 40% stands showed the highest extraction rate of 109.1%, while thinning 20% showed 87.1%, thinning 10% showed 63.5%, and control sites showed 56.0% of accuracy. As a result of tree height extraction, thinning 40% showed 1.43m error compared with field survey data, while thinning 20% showed 1.73 m, thinning 10% showed 1.88 m, and control sites showed the largest error of 2.22 m.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1669-1683
• /
• 2021

AI intelligent agriculture and digital agriculture are important for the science of agriculture. Leaf chlorophyll contents(LCC) are one of the most important indicators to determine the growth status of vegetable crops. In this study, a support vector machine (SVM) regression model was produced using an unmanned aerial vehicle-based RGB camera and a multispectral (MSP) sensor for onions and garlic, and the LCC estimation applicability of the RGB camera was reviewed by comparing it with the MSP sensor. As a result of this study, the RGB-based LCC model showed lower results than the MSP-based LCC model with an average R² of 0.09, RMSE 18.66, and nRMSE 3.46%. However, the difference in accuracy between the two sensors was not large, and the accuracy did not drop significantly when compared with previous studies using various sensors and algorithms. In addition, the RGB-based LCC model reflects the field LCC trend well when compared with the actual measured value, but it tends to be underestimated at high chlorophyll concentrations. It was possible to confirm the applicability of the LCC estimation with RGB considering the economic feasibility and versatility of the RGB camera. The results obtained from this study are expected to be usefully utilized in digital agriculture as AI intelligent agriculture technology that applies artificial intelligence and big data convergence technology.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.8
• /
• pp.15-22
• /
• 2019

Awireless sensor network utilizes small sensors with a low cost and low power being deployed over a wide area. They monitor the surrounding environment and gather the associated information to transmit it to a base station via multi-hop transmission. Most of the research has mainly focused on static sensors that are located in a fixed position. Unlike a wireless sensor network based on static sensors, we can exploit drone-based technologies for more efficient wireless networks in terms of coverage and energy. In this paper, we introduce a transmission power model and a video encoding power model to design the network environment. We also explain a priority mapping scheme, and deploy drones oriented for network coverage and energy consumption. Through our simulations, this research shows coverage and energy improvements in adrone-based wireless sensor network with fewer sensors, compared to astatic sensor-based wireless sensor network. Concretely, coverage increases by 30% for thedrone-based wireless sensor network with the same number of sensors. Moreover, we save an average of 25% with respect to the total energy consumption of the network while maintaining the coverage required.