• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.565-572
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• 2018

Recently, UAVs (Unmanned Aerial Vehicles) or drones have gained popularity for the engineering surveying and mapping because they enable the rapid data acquisition and processing as well as their operation cost is low. The applicable fields become much wider including the topographic monitoring, agriculture, and forestry. It is reported that the high geospatial accuracy is achievable with the drone photogrammetry for many applications. However most studies reported the best achievable mapping results using well-distributed ground control points though some studies investigated the impact of control points on the accuracy. In this study, we focused on the drone mapping of corridors such as roads and pipelines. The distribution and the number of control points along the corridor were diversified for the accuracy assessment. In addition, the effects of the camera self-calibration and the number of the image strips were also studied. The experimental results showed that the biased distribution of ground control points has more negative impact on the accuracy compared to the density of points. The prior camera calibration was favored than the on-the-fly self-calibration that may produce poor positional accuracy for the case of less or biased control points. In addition, increasing the number of strips along the corridor was not helpful to increase the positional accuracy.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.1
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• pp.69-80
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• 2021

The new town in Korea, developed as a large-scale housing plan, has created urban ecological corridors to provide habitat and movement routes to wildlife and to promote natural ecological flow. This study aimed to investigate the use of wildlife in 10 ecological corridors in Gwanggyo New Town through camera trap technology and confirm effectiveness by identifying environmental factors affecting the use of wildlife's urban ecological corridors. Our researchers installed 20 unmanned sensor cameras at each the entrance and exit of the ecological corridors, and monitored urban wildlife for 10 weeks. According to the monioring results, the main species in Gwanggyo New Town were identified not only raccons, cats, water deer, korean hare and avain but also magpies, dove, eurasian tree sparrow, ring-necked pheasant, and eurasian jay. The number of uses ecological corridors of urban residents was 801(13.49%), as high as that of urban wildlife (1,140, 19.20%), which was judged to have disturbed the use of ecological corridors by wildlife. However, most dominant species of urban wildlife are nocturnal so that, it was judged that they share home range with urban residents at a time interval. In addition, according to the correlation analysis results between the mammal using rate of the urban ecological corridors and environmental factors(ecological corridor-specific length, ecological corridor-specific width, cover degree, shielding degree, connected green area, separation of movement routes, and presence of streetlights), environmental factors were not statistically significant. However, the more the area of green space connected to ecological corridors, the more increasing the mammal using rate of ecological corridor(r=0.71, p<0.05). Therefore, the area of green space connected to the ecological corridors that is associated with rate of wildlife using corridors should be considered as a priority when developing an urban ecological corridors. In the future, this study will extend the observation period of the ecological corridors and continuously accumulate data by adding the number of observation cameras. Furthermore, it is expected that the results of this study can be used as basic data for the standards for urban ecological corridors installation.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.735-747
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• 2020

Recent advanced UAV (Unmanned Aerial Vehicle) technology supply new opportunities for estimating crop condition using high resolution imagery. We analyzed the diurnal change of reflectance and NDVI (Normalized Difference Vegetation Index) in UAV imagery for crop monitoring in clear day condition. Multi-spectral images were obtained from a 5-band multi-spectral camera mounted on rotary wing UAV. Reflectance were derived by the direct method using down-welling irradiance measurement. Reflectance using UAV imagery on calibration tarp, concrete and crop experimental sites did not show stable by time and daily reproducible values. But the CV (Coefficient of Variation) of diurnal NDVI on crop experimental sites was less than 5%. As a result of comparing NDVI at the similar time for two day, the daily mean average ratio of error showed a difference of 0.62 to 3.97%. Therefore, it is considered that NDVI using UAV imagery can be used for time series crop monitoring.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.3
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• pp.13-21
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• 2004

The dissemination of commercial satellite images. which have the high spatial resolution such as aerial photos, are the active trend in remote sensing community because of the recent development in satellite and sensor technology. Such high resolution satellite images provide a unique tool for the monitoring of ongoing urban land use change. Especially KOMPSAT-1, which was launched at December 1999 and successfully operated up to now, provides repeatedly panchromatic images over Korean peninsula, which has the spatial resolution of 6.6m. Based upon this KOMPSAT-1 EOC image data we can try to analyze and assess the temporal urban land use change, which could not be done because lack of such data. The aim of this paper is to analyze and assess the spatial land use characteristics of Daejeon Metropolitan City based on KOMPSAT-1 EOC data. The land use map of year 2001 is generated through the modification of the year 2000 land use map, which is published by National Geographic Information Institute, using visual interpretation of KOMPSAT-1 EOC image which is acquired in year 2001. This study can be the start point of the time series analysis of the long term land use change monitoring mit KOMPSAT-1 EOC data.

• Journal of IKEEE
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• v.24 no.1
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• pp.302-310
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• 2020

In this paper, we have implemented the living waste analysis system based on IoT and AI(Artificial Intelligence), and proposed effective waste process and management method. The Jeju location have the strong point to devise a stratagem and estimate waste quantization, rather than others. Especially, we can recognized the amount variation of waste to the residence people compare to the sightseer number, and the good example a specific waste duty. Thus this paper have developed the IoT device for interconnecting the existed CCTV camera, and use the AI algorithm to analysis the waste image. By using these decision of image analysis, we can inform their deal commend and a decided information to the map of the waste cars. In order to evaluate the performance of IoT, we have experimented the electromagnetic compatibility under a national official authorization KN-32, KN61000-4-2~6, and obtained the stable experimental results. In the further experimental results, we can applicable for an data structure for precise definition command by using the simulated several waste image with artificial intelligence algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.3
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• pp.255-263
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• 2014

Today, a number of CCTV on the highway is to observe the flow of traffics. There have been a number of studies where traffic data (e.g., the speed of vehicles and the amount of traffic on the road) are transferred back to the centralized server so that an appropriate action can be taken. This paper introduces a system that detects the changes of traffic flows caused by an accident or unexpected stopping (i.e., vehicle remains idle) by monitoring each lane separately. The traffic flows of each lane are level spacing curve that shows Wigner distribution for location vector. Applying calogero-moser system and Hamiltonian system, probability equation for each level-spacing curve is derived. The high level of modification of the signal means that the lane is in accident situation. This is different from previous studies in that it does more than looking for the signal from only one lane, now it is able to detect an accident in entire flow of traffic. In process of monitoring traffic flow of each lane, when camera recognizes a shadow of vehicle as a vehicle, it will affect the accident detecting capability. To prevent this from happening, the study introduces how to get rid of such shadow. The system using Basian network method is being compared for capability evaluation of the system of the study. As a result, the system of the study appeared to be better in performance in detecting the modification of traffic flow caused by idle vehicle.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.5
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• pp.271-279
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• 2014

In this paper, the two design techniques of the embedded system which provides a wireless image observation with temporary ad-hoc network are proposed and developed. The first method is based on the embedded system design technique for a nearly real-time wireless short observation application, having a specific remote monitoring node with a built-in image processing function, and having the maximum rate of 1 fps (frame per second) wireless image transmission capability of a $160{\times}128$size image. The second technique uses the embedded system for a general wireless long observation application, consisting of the main node, the remote monitoring node, and the system controller with built-in image processing function, and the capability of the wireless image transmission rate of 1/3 fps. The proposed system uses the wireless ad-hoc network which is widely accepted as a short range, low power, and bidirectional digital communication, the hardware are consisted of the general developed modules, a small digital camera, and a PC, and the embedded software based upon the Zigbee stack and the user interface software are developed and tested on the implemented module. The wireless environment analysis and the performance results are presented.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1149-1160
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• 2019

In this study, two radiometric correction methods deriving reflectance from UAV multispectral image for monitoring forest vegetation were applied and evaluated. Multispectral images were obtained from a small multispectral camera having 5 spectral bands. Reflectance were derived by applying the two methods: (1) the direct method using downwelling irradiance measurement and (2) the empirical line correction method by linking a set of field reflectance measured simultaneous with the image capture. Field reflectance were obtained using a spectroradiometer during the flight and used for building the linear equation for the empirical method and for the validation of image reflectance derived. Although both methods provided the high correlations between field reflectance and image-derived reflectance, their distributions were somewhat different. While the direct method provided rather stable and consistent distribution of reflectance all over the entire image area, the empirical method showed very unstable and inconsistent reflectance distribution. The direct method would be more appropriate for relatively wide area that requires more time to acquire image and may vary in downwelling irradiance and atmospheric conditions.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.701-714
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• 2018

The purpose of this study is to show the possibility of 3-dimensional modeling of open-pit limestone mine by using a rotary-wing unmanned aerial vehicle, a drone, and to estimate the amount of mining before and after mining of limestone by explosive blasting. Analysis of the image duplication of the mine has shown that it is possible to achieve high image quality. Analysis of each axis error at the shooting position after analyzing the distortions through camera calibration was shown the allowable range. As a result of estimating the amount of mining before and after explosive blasting, it was possible to estimate the amount of mining of a wide range quickly and accurately in a relatively short time. In conclusion, it is considered that the drone of a rotary-wing unmanned aerial vehicle can be usefully used for the monitoring of open-pit limestone mines and the estimation of the amount of mining. Furthermore, it is expected that this method will be utilized for periodic monitoring of construction sites and road slopes as well as open-pit mines in the future.