• Journal of Environmental Impact Assessment
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• v.18 no.5
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• pp.281-291
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• 2009

In Total Water Pollutant Load Management System of Korea, unit load approach based on land register data is currently used for the estimation of non-point pollutant load. However, a problem raised that land register data could not always reflect the actual land surface coverages which determine runoff characteristics of non-point pollution sources. As a way to overcome this, we tried to establish quantitative relationships between the aerial images (0.4m resolution) which reflect actual land surface coverages and the land registration maps according to the 19 major designated land-use categories in Kyeongan watershed. Analyses showed different relationships according to the land-use categories. Only a few land-use categories including forestry, road and river showed essentially identical and some categories such as orchard, parking lot and sport utility site showed no relationships at all between image data and land register data. Except for the two cases, all the other categories showed statistically significant linear relationships between image data and land register data. The analyses indicate that using high resolution aerial maps is a better way to estimate non-point pollutant load. If the aerial maps are not available, application of the linear relationships as conversion factors of land register data to image data could be an possible option to estimate non-point pollutant loads for the specific land-use categories in Kyeongan watershed.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.453-454
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• 2003

Most research materials (data), which are used for the study of digital mapping and digital elevation model (DEM) in the field of Remote Sensing and Aerial Photogrammetry are aerial photographs and satellite images. Additionally, they are also used for National land mapping, National land management, environment management, military purposes, resource exploration and Earth surface analysis etc. Although aerial photographs have high resolution, the data, which they contain, are not used for environment exploration that requires continuous observation because of problems caused by its coastline, as well as single - spectral and long-term periodic image. In addition to this, they are difficult to interpret precisely because Satellite Images are influenced by atmospheric phenomena at the time of photographing, and have by far much lower resolution than existing aerial photographs, while they have a great practical usability because they are mulitispectral images. The PKNU 2 is an aerial photographing system that is made to compensate with the weak points of existing aerial photograph and satellite images. It is able to take pictures of very high resolution using a color digital camera with 6 million pixels and a color infrared camera, and can take perpendicular photographs because PKNU 2 system has equipment that makes the cameras stay level. Moreover, it is very cheap to take pictures by using super light aircraft as a platform. It has much higher resolution than exiting aerial photographs and satellite images because it flies at a low altitude about 800m. The PKNU 2 can obtain multispectral images of visible to near infrared band so that it is good to manage environment and to make a classified diagram of vegetation.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.123-129
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• 2006

High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a asphalt road boundary extraction technique that combines information from aerial color image and LIDAR (LIght Detection And Ranging) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LEAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadow path reconstruction algorithms. After that, asphalt road boundary extraction is implemented by segmentation and edge detection. Finally, asphalt road boundary lines are extracted as vector data by vectorization technique. The experimental results showed that this approach was effective and great potential advantages.

• The Journal of the Korea Contents Association
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• v.8 no.3
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• pp.270-278
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• 2008

This study : An analysis of position accuracy of topography according to using LiDAR data, aerial photo and digital map for a study area was conducted. The study area was selected in Hadan area, Sahagu, Busan aerial LiDAR data and aerial photo in the scales of 1:20,000, which are high tech surveying ways were used. The final digital orthoimage according to orientation process for each image and image resampling was producted. Using it, a checkpoint was chosen, information about the checkpoints selected was extracted, a coordinate of Horizontal Position through the screen digitizing was also extracted. Both the coordinates of LiDAR data and aerial photo using digital map in the scales of 1:20,000 announced to the public from NGII(National Geographic Information Institute) were gradually compared and analyzed. Based on the digital map, as a result of being both compared and analyzed, it has shown to us that horizontal position of aerial photo is more accurate than that of aerial LiDAR surveying (RMSE-building x:24cm, y:26cm).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.249-260
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• 2009

The georeferencing accuracy of the sensory data acquired by an aerial monitoring system heavily depends on the performance of the GPS/IMU mounted on the system. The employment of a high performance but expensive GPS/IMU unit causes to increase the developmental cost of the overall system. In this study, we simulate the images and GPS/IMU data acquired by an UAV-based aerial monitoring system using an inexpensive integrated GPS/IMU of a MEMS type, and perform the image georeferencing by applying the aerial triangulation to the simulated sensory data without any GCP. The image georeferencing results are then analyzed to assess the accuracy of the estimated exterior orientation parameters of the images and ground points coordinates. The analysis indicates that the RMSEs of the exterior orientation parameters and ground point coordinates is significantly decreased by about 90% in comparison with those resulted from the direct georeferencing without the aerial triangulation. From this study, we confirmed the high possibility to develop a low-cost real-time aerial monitoring system.

• Journal of Korean Society for Geospatial Information Science
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• v.5 no.2 s.10
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• pp.207-220
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• 1997

This study aims to develope the production method of digital image map of high capable utiliy and terrain interpretability using aerial photo and Geospatial Information System. Theory and efficient practical method was studied to generate tile digital image map with low-cost personal computer system using the merging procedure of raster scanned aerial photo and vector topographic map. Determination theory of ground coordinates, digital image processing, production of digital elevation model was reviewed. And some chariteristics of digital image map, image collection method and significant concepts of digital image processing was studied. Also input and output way of image data to generate the digital image nap, production method of orthophoto map using aerial photo through digital differential rectification was studied. As the result, digital image map was produced and analyzed through the above mentioned procedures.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.5
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• pp.590-598
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• 2019

Aerial photographs taken by an image sensor fixed on a flight body, e.g. without a gimbal, are generally distorted according to its attitude, altitude and angle of view in flight. This can result in a significant difficulty of analyzing geometric information which should be integrated for numerous still frames. In this study, a simulation method of observation performance that uses geometric relationships between navigation data and image data is suggested, and this method is shown to be very useful for easily examining the integrated information such as the total range of photography, the time of target acquisition, etc.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.1
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• pp.11-21
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• 2015

An Ortho-image is the production of removed geometrical displacement, which is generated the aerial image distortion and the relief displacement, etc., using the DSM (Digital Surface Model). Accordingly, the resolution of raw image and the accuracy of DSM will has significant impacts on the ortho-image accuracy. Since the latest DMCII250 aerial camera delivers the high resolution images with five centimeters Ground Sampling Distance(GSD), it expects to generate the high density point clouds and the high quality ortho-images. Therefore, this research has planned for reviewing the potentiality and accuracy of high quality ortho-image production. Following to proceed the research, DSM has been produced through the high density point cloud extracted from DMCII250 aerial image to supply of high density DSM by creation of ortho-image. The research results has been identified that images with the DSM brought out higher degrees in positional accuracy and quality of ortho-image, compared with the ortho-image, produced from the existing digital terrain map or DSM data.

• Proceedings of the KSRS Conference
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• v.2
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• pp.764-767
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• 2006

It can be important framework data to monitor the change of land-use pattern of river boundary in design and management of river. This study analyzed the change of land-use pattern of Gab- and Yudeung River using time-series aerial images. To do this, we carried out radiation and geometric correction of image, and estimated land-use changes in inland and floodplain. As the analysis of inland, the ratio of residential, commercial, industrial, educational and public area, that is urbanized element, increases, but that of agricultural area shows a decline on the basis of 1990. Also, Minimum Distance Method, which is a kind of supervised classification method, is applied to extract water-body and sand bar layer in floodplain. As the analysis of land-use, the ratio of level-upped riverside land and water-body increases, but that of sand bar decreases. These time-series land use information can be important decision making data to evaluate the urbanization of river boundary, and especially it gives us goodness in river development project such as the composition of ecological habitat.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.499-509
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• 2020

This study explores how to build object classification learning data based on artificial intelligence. The data has been investigated recently in image classification fields and, in turn, has a great potential to use. In order to recognize and extract relatively accurate objects using artificial intelligence, a large amount of learning data is required to be used in artificial intelligence algorithms. However, currently, there are not enough datasets for object recognition learning to share and utilize. In addition, generating data requires long hours of work, high expenses and labor. Therefore, in the present study, a small amount of initial aerial image learning data was used in the GAN (Generative Adversarial Network)-based generator network in order to establish image learning data. Moreover, the experiment also evaluated its quality in order to utilize additional learning datasets. The method of oversampling learning data using GAN can complement the amount of learning data, which have a crucial influence on deep learning data. As a result, this method is expected to be effective particularly with insufficient initial datasets.