• 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.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.515-517
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• 2007

In this study we attempted to estimate damage scope such as bridges destruction, farmland deformation, forest damage, etc occurred by typhoon using two digital aerial images for future high-resolution Kompsat-3 applications. The process procedures are followings: First, image registration between time-different aerial images was implemented. In this process one image was geometrically corrected by image-to-image registration. Second, image classification was done according to 4 classes. Finally through the comparison of classified two images the area of damage by flood and storm was approximately calculated. These results showed that it is possible to estimate the damage scale relatively rapidly using high-resolution images.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.187-190
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• 2007

This study is for analyzing best band in image matching using correlation coefficient of left and right images of stereo image pair, lot red, green, blue band images separated from color aerial photo and gray image converted from the same color aerial photo image. The image matching is applied to construct Digital Elevation Model(DEM) or terrain data. The correlation coefficients and variation by change of pixel patch size are computed from pixel patches of which sizes are $11{\times}11{\sim}101{\times}101$. Consequently, the correlation coefficient in red band image is highest. The lowest is in blue band. Therefore, to construct terrain data using image matching, the red band image is preferable. As the size of pixel patch is growing, the correlation coefficient is increasing. But increasing rate declines from $51{\times}51$ image patch size and above. It is proved that the smaller pixel patch size than $51{\times}51$ is applied to construct terrain data using image matching.

• 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.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.747-761
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• 2021

With the increasing application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become an essential engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial images based on the YOLOv4 deep learning algorithm is presented. At present, the most known datasets are VOC (The PASCAL Visual Object Classes Challenge), ImageNet, and COCO (Microsoft Common Objects in Context), which comply with the vehicle detection from UAV. An integrated dataset not only reflects its quantity and photo quality but also its diversity which affects the detection accuracy. The method integrates three public aerial image datasets VAID, UAVD, DOTA suitable for YOLOv4. The training model presents good test results especially for small objects, rotating objects, as well as compact and dense objects, and meets the real-time detection requirements. For future work, we will integrate one more aerial image dataset acquired by our lab to increase the number and diversity of training samples, at the same time, while meeting the real-time requirements.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.116-124
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• 2008

This study presents the application of multi-temporal and multi-scale panchromatic aerial photos for change detection in a small urban area. For aerial photos of the scale of 1:20,000 taken in 1987 and 1996 and the scale of 1:37,500 taken in 2000. Pre-processing that make the same conditions to all of the aerial photos was carried out through geometric correction, registration, contrasting, resamplimg, and mosaicking and then change detection were carried out respectively by image differencing and image ratio techniques. As a result, the change of urban features and landcover were able to be detected from panchromatic aerial photos that is single-band images and then the detected change results were compared between both techniques.

• 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.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.169-173
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• 2007

Nowadays the aerial photos is using to get the information around our spatial environment and it increases by geometric progression in many fields. The aerial photos need in a simple object such as cartography and ground covey classification and also in a social objects such as the city plan, environment, disaster, transportation etc. However, the shadow, which includes when taking the aerial photos, makes a trouble to interpret the ground information, and also users, who need the photos in their field tasks, have restriction. This study, for removing the shadow, uses the single image and the image without the source of image and taking situation. Also, this study present clustering algorism based on HIS color model that use Hue, Saturation and Intensity, especially this study used I(intensity) to extract shadow area from image. And finally by filtering in Fourier frequency domain creates the intrinsic image which recovers the 3-D color information and removes the shadow.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.4
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• pp.48-57
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• 1992

This paper addresses the estimation of the shift vector from aerial image sequences. The conventional feature-based and area-based matching methods are simulated for determining the suitable image registration scheme. Computer simulations show that the feature-based matching schemes based on the co-occurrence matrix, autoregressive model, and edge information do not give a reliable matching for aerial image sequences which do not have a suitable statistical model or significant features. In area-based matching methods we try various similarity functions for a matching measure and discuss the factors determining the matching accuracy. To reduce the estimation error of the shift vector we propose the reference window selection scheme. We also discuss the performance of the proposed algorithm based on the simulation results.