• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.709-717
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• 2014

The objective of this study is to analyze the degree of correspondences among corners found in building facade imagery when the projective transformation parameters are applied to. Additionally, an appropriate corner detection operator is determined through experiments. Modeling of the shape of a building has been studied in numerous approaches using various type of data such as aerial imagery, aerial lidar scanner imagery, terrestrial imagery, and terrestrial lidar imagery. This study compared the Harris operator with FAST operator and found that the Harris operator is superior in extracting major corner points. After extracting corners using the Harris operator and assessing the degree of correspondence among corners in difference images, real corresponding corners were found to be located in the closest distance. The experiment of the projective transformation with varying corners shows that more corner control points with a good distribution enhances the accuracy of the correspondences.

• Journal of Astronomy and Space Sciences
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• v.11 no.1
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• pp.53-70
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• 1994

The Korean sounding rockets(KSR420S-1, -2) equipped with ozone detectors have b3en launched at An-heung, Chungchungnam-do, on June 4 and September 1, 1993, respectively. The ozone detector is used to measure the attenuation of solar UV radiation for various frequency bands in the stratosphere, to obtain vertical profiles of the ozone number density in the stratosphere. They confirm that the maximum ozone densities occur near 25 km, which is quite consistent with the mean value in the mid-latitude region. Our results from KSR420S-1 and -2 are compared with the other observation data from the Dobson spectrophotometer at Yonsei Univ., the LIDAR at Kyunghee Univ., the SBUV from Nimbus satellite, and the TOVS from NOAA satellite, which were performed simultaneously with the sounding rocket experiments.

• Spatial Information Research
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• v.20 no.6
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• pp.45-57
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• 2012

The street trees in the urban areas provide an comfortable environment to the pedestrians and drivers and play important roles to absorb the carbons. Therefore, it is necessary to acquire and manage efficiently the location, height, and crown width of street trees. This study suggests a methodology to provide quantitative information of the street trees in urban areas including the quantity, location, height, and crown width of the trees. Therefore, it is more appropriate to add functionality of changing size of the crown width of the trees in the method. In addition, the positions of the street trees were selected using the fact that street trees are generally planted along the road in a straight line. An experiment on extracting street trees was conducted in parts of Osan-si, Gyeonggi-do and the suitability of the suggested methodology was evaluated by comparing the results to a 1/1,000 digital map. Through the experimental results, the minimum, maximum, and the root mean square errors of the position of street trees were 0.5m, 1.9m, and approximately ${\pm}0.4m$, respectively.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.183-191
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• 2015

If the damage analysis on river-side facilities such as dam, river bank structures and bridges caused by disasters such as typhoon, flood, etc. becomes available, it can be a great help for disaster recovery and decision-making. In this research, We tried to extract a Digital Surface Model (DSM) and analyze the accuracy from Unmanned Air Vehicle (UAV) images on river-side facilities. We tried to apply stereo image-based matching technique, then extracted match results were united with one mosaic DSM. The accuracy was verified compared with a DSM derived from LIDAR data. Overall accuracy was around 3m of absolute and root mean square error. As an analysis result, we confirmed that exterior orientation parameters exerted an influence to DSM accuracy. For more accurate DSM generation, accurate EO parameters are necessary and effective interpolation and post process technique needs to be developed. And the damage analysis simulation with DSM has to be performed in the future.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.623-629
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• 2018

In this paper, we propose a real-time obstacle detection and avoidance path generation algorithm for UAV. 2-D Lidar is used to detect obstacles, and the detected obstacle data is used to generate real-time histogram for local avoidance path and a 2-D SLAM map used for global avoidance path generation to the target point. The VFH algorithm for local avoidance path generation generates a real-time histogram of how much the obstacles are distributed in the vector direction and distance, and this histogram is used to generate the local avoidance path when detecting near fixed or dynamic obstacles. We propose an algorithm, called modified $RRT^*-Smart$, to overcome existing limitations. That generates global avoidance path to the target point by creating lower costs because nodes are checked whether or not straight path to a target point, and given arbitrary lengths and directionality to the target points when nodes are created. In this paper, we prove the efficient avoidance maneuvering through various simulation experiment environment by creating efficient avoidance paths.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.17-26
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• 2021

Recently, COVID-19 has spread and time to stay at home has been increasing in accordance with quarantine guidelines of the government such as recommendations to refrain from going out. As a result, the number of single-person households staying at home is also increasingsingle-person households are less likely to be notified to the outside world in times of emergency than multi-person households. This study collects various situations occurring in the home with lidar, image, and voice sensors and analyzes the data according to the sensors through their respective algorithms. Using this method, we analyzed abnormal patterns such as emergency situations and conducted research to detect abnormal signs in humans. Artificial intelligence algorithms that detect abnormalities in people by each sensor were studied and the accuracy of anomaly detection was measured according to the sensor. Furthermore, this work proposes a fusion method that complements the pros and cons between sensors by experimenting with the detectability of sensors for various situations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.584-590
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• 2020

Long-term use of digital screens in daily life can lead to computer vision syndrome including symptoms such as eye strain, dry eyes, and headaches. To prevent computer vision syndrome, it is important to limit screen usage time and take frequent breaks. There are a variety of applications that can help users know the screen usage time. However, these apps are limited because users see various screens such as desktops, laptops, and tablets as well as smartphone screens. In this paper, we propose and evaluate machine learning-based models that detect the screen device in use using color, IMU and lidar sensor data. Our evaluation shows that neural network-based models show relatively high F1 scores compared to traditional machine learning models. Among neural network-based models, the MLP and CNN-based models have higher scores than the LSTM-based model. The RF model shows the best result among the traditional machine learning models, followed by the SVM model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.849-856
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• 2020

Recently, as the number of high-height vehicles such as double-decker buses has increased, collision accidents have occurred in bridges and tunnels due to the deviation from the designated routes and driver's carelessness. In the case of the existing front collision warning system, it is limited to vehicles and pedestrians, so it is difficult to use it as a pass height warning system for the high height vehicles. In this paper, we propose a system that generates a warning by determining the correlation and time series characteristics of data for each segment using multiple lidar sensors and then determining the possibility of collision in the upper part of the vehicle. Also, the proposed system confirmed the proper operation through a real-time driving test and a system performance evaluation by the Korea Automobile Testing & Research Institute.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.21-26
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• 2008

A method for processing huge airborne laser scanned data using parallel computing and virtual grid is proposed and the method is tested by generating raster DSM(Digital Surface Model) with IDW(Inverse Distance Weighting). Parallelism is involved for fast interpolation of huge point data and virtual grid is adopted for enhancing searching efficiency of irregularly distributed point data. Processing time was checked for the method using cluster constituted of one master node and six slave nodes, resulting in efficiency near to 1 and load scalability property. Also large data which cannot be processed with a sole system was processed with cluster system.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.661-670
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• 2012

Urban forests provide great ecosystem services to population in metropolitan areas even though they occupy little green space in a huge gray landscape. Unfortunately, urbanization inherently results in threatening the green infrastructure, and the recent urbanization trends drew great attention of scientists and policy makers on how to preserve or restore green infrastructure in metropolitan area. For this reason, mapping the spatial distribution of the green infrastructure is important in urban environments since the resulting map helps us identify hot green spots and set up long term plan on how to preserve or restore green infrastructure in urban environments. As a preliminary step for mapping green infrastructure utilizing multi-source remote sensing data in urban environments, the objective of this study is to map vegetation volume by fusing LiDAR and multispectral data in urban environments. Multispectral imageries are used to identify the two dimensional distribution of green infrastructure, while LiDAR data are utilized to characterize the vertical structure of the identified green structure. Vegetation volume was calculated over the metropolitan Chicago city area, and the vegetation volume was summarized over 16 NLCD classes. The experimental results indicated that vegetation volume varies greatly even in the same land cover class, and traditional land cover map based above ground biomass estimation approach may introduce bias in the estimation results.