• 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 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.37 no.6_1
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• pp.1757-1766
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• 2021

The green coverage ratio is the ratio of the land area to green coverage area, and it is used as a practical urban greening index. The green coverage ratio is calculated based on the land cover map, but low spatial resolution and inconsistent production cycle of land cover map make it difficult to calculate the correct green coverage area and analyze the precise green coverage. Therefore, this study proposes a new method to calculate green coverage area using aerial images and deep neural networks. Green coverage ratio can be quickly calculated using manned aerial images acquired by local governments, but precise analysis is difficult because components of image such as acquisition date, resolution, and sensors cannot be selected and modified. This limitation can be supplemented by using an unmanned aerial vehicle that can mount various sensors and acquire high-resolution images due to low-altitude flight. In this study, we proposed a method to calculate green coverage ratio from manned or unmanned aerial images, and experimentally verified the proposed method. Aerial images enable precise analysis by high resolution and relatively constant cycles, and deep learning can automatically detect green coverage area in aerial images. Local governments acquire manned aerial images for various purposes every year and we can utilize them to calculate green coverage ratio quickly. However, acquired manned aerial images may be difficult to accurately analyze because details such as acquisition date, resolution, and sensors cannot be selected. These limitations can be supplemented by using unmanned aerial vehicles that can mount various sensors and acquire high-resolution images due to low-altitude flight. Accordingly, the green coverage ratio was calculated from the two aerial images, and as a result, it could be calculated with high accuracy from all green types. However, the green coverage ratio calculated from manned aerial images had limitations in complex environments. The unmanned aerial images used to compensate for this were able to calculate a high accuracy of green coverage ratio even in complex environments, and more precise green area detection was possible through additional band images. In the future, it is expected that the rust rate can be calculated effectively by using the newly acquired unmanned aerial imagery supplementary to the existing manned aerial imagery.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.1
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• pp.80-93
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• 2016

Parcel boundary demarcation in agricultural area is commonly performed by terrestrial surveying methods, which have been pointed out as drawbacks to require consuming too much time and heavy expenditure. With the developments of high performance digital aerial cameras, however, studies on cadastral boundary demarcation with an aerial photogrammetric method attract a great attention in recent years. In this paper, an approach is presented to rapidly demarcate parcel boundaries coinciding with real ground ones in agricultural areas by extracting boundaries from the high resolution aerial orthoimages based on aerial targets. In order to investigate the feasibility of the proposed method, the accuracy of coordinates and area of parcel boundaries extracted from the aerial targets appeared in orthoimages compared with that of terrestrial boundary surveying results over the selected two test agricultural areas. Aerial image data were processed taken by a ADS80 digital camera with a GSD of 8cm in Changwon region, and by a DMCII camera with a GSD of 5cm in Suwon respectively. The result shows that the accuracy of parcel demarcation using aerial images is within the tolerance limits of coordinates and areas compared with that of terrestrial surveying. The proposed method using aerial target-based high resolution aerial images is therefore expected to be usefully applied in the agricultural parcel demarcation.

• 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 Agricultural Science
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• v.42 no.3
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• pp.269-276
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• 2015

Unmanned Aerial Vehicle (UAV) has several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude, they can obtain good quality images even in cloudy weather. In this paper, we discussed the state-of-the-art of the domestic and international use of UAV in agricultural sector as well as assessed its utilization and applicability for agricultural environment in Korea. Association of robotic, computer vision and geomatic technologies have established a new paradigm of low-altitude aerial remote sensing that has now been receiving attention from researchers all over the world. In a field study, it has been found that use of UAV imagery in an agricultural subsidy program can reduce the farmers' complain and provide objective evidence. UAV high resolution photography can also be helpful in monitoring the disposal zone for animal carcasses. Due to its expeditiousness and accuracy, UAV imagery can be a very useful tool to evaluate the damage in case of an agricultural disaster for both parties insurance companies and the farmers. Also high spatial and temporal resolution in UAV system can increase the prediction accuracy which in turn help to maintain the agricultural supply and demand chain.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.173-179
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• 2015

In this paper, object-based classification of urban areas based on a combination of information from lidar and aerial images is introduced. High resolution images are frequently used in automatic classification, making use of the spectral characteristics of the features under study. However, in urban areas, pixel-based classification can be difficult since building colors differ and the shadows of buildings can obscure building segmentation. Therefore, if the boundaries of buildings can be extracted from lidar, this information could improve the accuracy of urban area classifications. In the data processing stage, lidar data and the aerial image are co-registered into the same coordinate system, and a local maxima filter is used for the building segmentation of lidar data, which are then converted into an image containing only building information. Then, multiresolution segmentation is achieved using a scale parameter, and a color and shape factor; a compactness factor and a layer weight are implemented for the classification using a class hierarchy. Results indicate that lidar can provide useful additional data when combined with high resolution images in the object-oriented hierarchical classification of urban areas.

• Korean Journal of Remote Sensing
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• v.18 no.6
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• pp.327-336
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• 2002

Ever since high resolution satellites were launched, high-resolution satellite images have been utilized in many areas. This paper proposed methods of generating simulated satellite image using DEM(Digital Elevation Model) and digital image such as aerial photograph. There are two methods proposed in the paper: one is Direct-Indirect method and the other Indirect-Indirect, method. It is assumed that satellite attitude is not changing and perspective center is moving in the direction of flight while image is captured. The proposed methods were implemented with aerial photograph, DEM data, arbitrary orbit parameters and attitude parameters of high resolution satellite image under generation. Furthermore, for the stereo viewing, different orientation parameters and perspective center were tested for generating simulated satellite image. In addition, the quality and accuracy of the simulated satellite image generated by the proposed methods were analyzed.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1063-1071
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• 2023

Unmanned aerial vehicles (UAVs, drones) are becoming increasingly useful in a variety of fields. Advances in UAV and camera technology have made it possible to equip them with ultra-high resolution sensors and capture images at low altitudes, which has improved the reliability and classification accuracy of object identification on the ground. The distinctive contribution of this study is the derivation of sensor-specific performance metrics (GRD/GSD), which shows that as the GSD increases with altitude, the GRD value also increases. In this study, we identified the characteristics of various onboard sensors and analysed the image quality (discrimination resolution) of aerial photography results using UAVs, and calculated the shooting conditions to obtain the discrimination resolution required for reading ground objects.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.1
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• pp.59-67
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• 2002

Recently, much demand of vector data have increased rapidly such as a digital map instead of traditional a paper map and the raster data such as a high-resolution orthoimage have been used for many GIS application with the advent of industrial high-resolution satellites and development of aerial optical sensor technologies. Aerial photogrammetric technologies using an airship can offer cost-effective and high-resolution color images as well as real time images, different from conventional remote sensing measurements. Also, it can acquire images easily and its processing procedure is short and simple relatively. On the other hand, it has often been used for the production of a small-scale land use map not required high accuracy, monitoring of linear infrastructure features through mosaicking strip images and construction of GIS data. Through this study, the developed aerial photogrammetric system using the airship expects to be applied to not only producing of scale 1:5, 000 digital map but also verifying, editing, and updating the digital map which was need to be reproduced. Further more, providing the various type of video-images, it expects to use many other GIS applications such as facilities management, scenery management and construction of GIS data for Urban area.