• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.6
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• pp.621-626
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• 2010

For geometric correction of high-resolution images, the authors matched corresponding objects between a large-scale digital map and a QuickBird image to obtain the coefficients of the first order polynomial. Proximity corrections were performed, using the Boolean operation, to perform automated matching accurately. The modified iterative closest point (ICP) algorithm was used between the point data of the surface linear objects and the point data of the edge objects of the image to determine accurate transformation coefficients. As a result of the automated geometric correction for the study site, an accuracy of 1.207 root mean square error (RMSE) per pixel was obtained.

• Journal of Conservation Science
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• v.27 no.3
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• pp.251-260
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• 2011

This study focus on the suggestion of standard legend, the process system on making method of deterioration map, the development of crack index (CI), and the evaluation techniques of surface and 3D deterioration rate for stone cultural heritage. The standard legends of deterioration forms were made using a common graphic program after crack, blistering, scaling, break-out, granular disintegration, and perforation were subdivided. The deterioration map improved accuracy and reliability on deterioration range using 3D digital restoration and high resolution photograph mapping technique. Also, quantitative deterioration evaluation of stone cultural heritage was carried out developing the crack index, and the 3D deterioration rate of a break-out part was calculated by virtual restoration modeling. As a quantitative deterioration evaluation of Magoksa Temple stone pagoda based on the results described above, the north face showed high deterioration rate of bursting crack (1.70), hair crack (1.34), scaling (20.2%) and break out (13.0%), and the 3D deterioration rate of first roof stone was 6.7%.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.277-292
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• 2020

The purpose of this study is to propose the possibility of precise surface sedimentary facies classification and a more accurate classification method by generating the large-scale map of surface sedimentary facies based on UAV data and object-based image analysis (OBIA) for Hwang-do tidal flat in Cheonsu bay. The very high resolution UAV data extracted factors that affect the classification of surface sedimentary facies, such as RGB ortho imagery, Digital elevation model (DEM), and tidal channel density, and analyzed the principal components of surface sedimentary facies through statistical analysis methods. Based on principal components, input data to be used for classification of surface sedimentary facies were divided into three cases such as (1) visible band spectrum, (2) topographical elevation and tidal channel density, (3) visible band spectrum and topographical elevation, tidal channel density. The object-based image analysis classification method was applied to map the classification of surface sedimentary facies according to conditions of input data. The surface sedimentary facies could be classified into a total of six sedimentary facies following the folk classification criteria. In addition, the use of visible band spectrum, topographical elevation, and tidal channel density enabled the most effective classification of surface sedimentary facies with a total accuracy of 63.04% and the Kappa coefficient of 0.54.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.3
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• pp.71-78
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• 2007

We suggest a multi-path propagation analysis method using DTM(Digital Terrain Map). Generally, the total signal strength at a target is calculated by adding the field propagated in free space and the field reflected from the ground surface. In this paper, we also consider the vertical reflections associated with the vertical surfaces such as precipitous cliffs and electricity pylons in the mountain area. In addition, we primarily take account the main slope of the ground surface to improve the accuracy of the total field density at the target.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.79-89
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• 2008

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series contaminated by noises resulted from mechanical problems or sensing environmental condition. There is also a high likelihood that during the data acquisition periods the target site corresponding to any given pixel may be covered by fog or cloud, thereby resulting in bad or missing observation. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. A feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. The experimental results of this simulation study show the potentiality of the proposed system to reconstruct the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather. This study provides fundamental information on the elements of the proposed system for right usage in application.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.2
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• pp.54-63
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• 2010

The intensive rainfall over 455 mm occurred between on 9 to 14 July 2009 triggered debris flows around the mountain area in Jecheon County. We mapped the debris flow area and estimated the debris flow volume using a high resolution digital elevation model (DEM) generated respectively from terrestrial LiDAR (Light Detection And Ranging) and topographic maps. For the LiDAR measurement, the terrestrial laser scanning system RIEGL LMS-Z390i which is equipped with GPS system and high-resolution digital camera were used. After the clipping and filtering, the point data generated by LiDAR scanning were overlapped with digital map and produced DEM after debris flow. The comparison between digital map and LiDAR scanning result showed the erosion and deposition volumes of about $17,586m^3$ and $7,520m^3$, respectively. The LiDAR data allowed comprehensive investigation of the morphological features present along the sliding surface and in the deposit areas.

• Journal of the Korean Society of Safety
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• v.28 no.4
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• pp.26-32
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• 2013

A digital image correlation(DIC) method is a whole-field measurement technique that acquires surface displacements and strains from images information which characterized a random speckle as intensity grey levels. Recently years, this DIC method is being developed and used increasingly in various research. In this study, we tried to apply to aluminum alloy(Al 6061-T6) using DIC method and strain gauge. DIC results demonstrated the usefulness and ability to determine a strain. The test specimen used in this study was an aluminum alloy(Al 6061-T6, thickness 1 mm). For a strain measurement, a strain gauge was attached at the center of a specimen. A specimen was lightly sprayed with a white paint and a black dot pattern was sprayed on its fully dried white surface to obtain a random speckle. The experimental apparatus used to perform the tensile test consisted of universal dynamic tester(5 kN; T.O. Co.) under displacement speed of 0.5, 1.0 and 3.0 mm/min. A Model 5100 B Scanner(V. Co.) used to obtain a strain. A CCD camera connected to a PC uses to record the images of the specimen surface. After acquisition, the images were transferred to PC where the DIC software was implemented. An acquired image was evaluated by the DIC program. DIC method for displacement and strain was suggests and it results show a good consistent remarkably. DIC results demonstrated the usefulness and ability to determine surface strain was better than by using classical measurements. The strain field measurement using a DIC is so useful that it can be applied to map strain distributions at a full area. DIC method can evaluate a strain change so it can predict a location of fracture. The findings of the investigation suggest that the DIC method is an efficient and reliable tool for full-field monitoring and detailed damage characterization of materials.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.49-58
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• 2012

Buildings become complex and diverse with time. It is difficult to extract individual buildings using only an optical image, because they have similar spectral characteristics to objects such as vegetation and roads. In this study, we propose a method to extract building area and boundary through integrating airborne Light Detection and Ranging(LiDAR) data and aerial images. Firstly, a binary edge map was generated using Edison edge detector after applying Adaptive dynamic range linear stretching radiometric enhancement algorithm to the aerial image. Secondly, building objects on airborne LiDAR data were extracted from normalized Digital Surface Model and aerial image. Then, a temporary building areas were extracted by overlaying the binary edge map and building objects extracted from LiDAR data. Finally, some building boundaries were additionally refined considering positional accuracy between LiDAR data and aerial image. The proposed method was applied to two experimental sites for validation. Through error matrix, F-measure, Jaccard coefficient, Yule coefficient, and Overall accuracy were calculated, and the values had a higher accuracy than 0.85.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.700-710
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• 2009

A computer vision technique of estimating the location of an unmanned ground vehicle is proposed. Identifying the location of the unmaned vehicle is very important task for automatic navigation of the vehicle. Conventional positioning sensors may fail to work properly in some real situations due to internal and external interferences. Given a DSM(Digital Surface Map), location of the vehicle can be estimated by the registration of the DSM and multiview range images obtained at the vehicle. Registration of the DSM and range images yields the 3D transformation from the coordinates of the range sensor to the reference coordinates of the DSM. To estimate the vehicle position, we first register a range image to the DSM coarsely and then refine the result. For coarse registration, we employ a fast random sample matching method. After the initial position is estimated and refined, all subsequent range images are registered by applying a pair-wise registration technique between range images. To reduce the accumulation error of pair-wise registration, we periodically refine the registration between range images and the DSM. Virtual environment is established to perform several experiments using a virtual vehicle. Range images are created based on the DSM by modeling a real 3D sensor. The vehicle moves along three different path while acquiring range images. Experimental results show that registration error is about under 1.3m in average.