• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1041-1047
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• 2017

There are many methods for surveying the status of a road, and the use of unmanned aerial vehicle (UAV) photo is one such method. When the UAV images are too large to be processed and suspected to be redundant, a texture extraction technique is used to transform the data into a reduced set of feature representations. This is an important task in 3D simulation using UAV images because a huge amount of data can be inputted. This paper presents a texture extraction method from UAV images to obtain high-resolution images of bridges. The proposed method is in three steps: firstly, we use the 3D bridge model from the V-World database; secondly, textures are extracted from oriented UAV images; and finally, the extracted textures from each image are blended. The result of our study can be used to update V-World textures to a high-resolution image.

• Journal of Korean Society of Rural Planning
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• v.27 no.4
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• pp.55-70
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• 2021

In this study, in the field of remote sensing, where the scope of application is rapidly expanding to fields such as land monitoring, disaster prediction, facility safety inspection, and maintenance of cultural properties, monitoring of rural space and surrounding environment using UAV is utilized. It was carried out to verify the possibility, and the following main results were derived. First, the aerial image taken with an unmanned aerial vehicle had a much higher image size and spatial resolution than the aerial image provided by the National Geographic Information Service. It was suitable for analysis due to its high accuracy. Second, the more the number of photographed photos and the more complex the terrain features, the more the point cloud included in the aerial image taken with the UAV was extracted. As the amount of point cloud increases, accurate 3D mapping is possible, For accurate 3D mapping, it is judged that a point cloud acquisition method for difficult-to-photograph parts in the air is required. Third, 3D mapping technology using point cloud is effective for monitoring rural space and rural resources because it enables observation and comparison of parts that cannot be read from general aerial images. Fourth, the digital elevation model(DEM) produced with aerial image taken with an UAV can visually express the altitude and shape of the topography of the study site, so it can be used as data to predict the effects of topographical changes due to changes in rural space. Therefore, it is possible to utilize various results using the data included in the aerial image taken by the UAV. In this study, the superiority of images acquired by UAV was verified by comparison with existing images, and the effect of 3D mapping on rural space monitoring was visually analyzed. If various types of spatial data such as GIS analysis and topographic map production are collected and utilized using data that can be acquired by unmanned aerial vehicles, it is expected to be used as basic data for rural planning to maintain and preserve the rural environment.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.4
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• pp.23-32
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• 2021

For agricultural water management, it is essential to establish the digital infrastructure data such as agricultural watershed, irrigated area and canal network in rural areas. Approximately 70,000 irrigation facilities in agricultural watershed, including reservoirs, pumping and draining stations, weirs, and tube wells have been installed in South Korea to enable the efficient management of agricultural water. The total length of irrigation and drainage canal network, important components of agricultural water supply, is 184,000 km. Major problem faced by irrigation facilities management is that these facilities are spread over an irrigated area at a low density and are difficult to access. In addition, the management of irrigation facilities suffers from missing or errors of spatial information and acquisition of limited range of data through direct survey. Therefore, it is necessary to establish and redefine accurate identification of irrigated areas and canal network using up-to-date high resolution images. In this study, previous existing data such as RIMS (Rural Infrastructure Management System), smart farm map, and land cover map were used to redefine irrigated area and canal network based on appropriate image data using satellite imagery, aerial imagery, and drone imagery. The results of the building the digital infrastructure in rural areas are expected to be utilized for efficient water allocation and planning, such as identifying areas of water shortage and monitoring spatiotemporal distribution of water supply by irrigated areas and irrigation canal network.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.475-478
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• 2007

This study presents the use of multi-temporal JERS-1 SAR images to extract the land cover information and possibility. So far, land cover information extracted by high resolution aerial photo and field survey. The study site was located in Non-san area. This study developed on multi-temporal land cover status monitoring and coherence information mapping can be processing by L band SAR image. From July, 1997 to October, 1998 JERS SAR images (9 scenes) coherence values are analyzed and then extracted land cover information factors, so on. This technique which forms the basis of what is called SAR Interferometry or InSAR for short has also been employed in spaceborne systems. In such systems the separation of the antennas, called the baseline is obtained by utilizing a single antenna in a repeat pass

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.495-506
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• 2017

This study was carried out to effectively perform relative color correction for high-resolution aerial ortho image. For this study, relative geometrical error between adjacent images was analyzed. The block sum method is proposed to reduce the relative geometrical error. We used the regression coefficients determined based on the block sum size to perform the color correction. As a result, it was confirmed that the relative color correction was visually performed well. Quantitative analysis was performed through histogram similarity analysis. It is proved that block sum method is useful for relative color correction. Particularly, the block sum size was very important to correct color based on the amount of relative geometrical error.

• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.159-169
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• 2003

This paper presents a method for automatic 3-d building reconstruction using high resolution aerial imagery. First, by using edge preserving filtering, noise is eliminated and then images are segmented by watershed algorithm, which preserves location of edge pixels. To extract line segments between control points from boundary of each region, we calculate curvature of each pixel on the boundary and then find the control points. Line segment linking is performed according to direction and length of line segments and the location of line segments is adjusted using gradient magnitudes of all pixels of the line segment. Coplanar grouping and pplygonal patch formation are performed per region by selecting 3-d line segments that are matched using epipolar geometry and flight information. The algorithm has been applied to high resolution aerial images and the results show accurate 3D building reconstruction.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.127-130
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• 2007

The increased availability of portable, low-cost, high resolution video devices has resulted in a rapid growth of the applications for video sequences. These video devices can be mounted in handhold unit, mobile unit and airborne platforms like maned or unmaned helicopter, plane, airship, etc. A core technique in use of video sequences is to align neighborhood video frames to each other or to reference images. For video sequences registration, we extracted interested points from aerial video sequences using Harris, $F{\square}rstner$, and KLT operators and implemented image matching using these points. As the result, we analysed image matching results for each operators and evaluated accuracy of aerial video registration.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.54-58
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• 2002

Mean curvature diffusion (MCD) is a selective smoothing technique that promotes smoothing within a region instead of smoothing across boundaries. By using mean curvature diffusion, noise is eliminated and edges are preserved. In this paper, we propose methods of automatic parameter selection and implementation for the MCD model coupled to min/max flow. The algorithm has been applied to high resolution aerial images and the results show that noise is eliminated and edges are preserved after removal of noise.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.87-97
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• 2023

Unmanned aerial vehicles (UAVs) can capture high-resolution imagery from a variety of viewing angles and altitudes; they are generally limited to collecting images of small scenes from larger regions. To improve the utility of UAV-appropriated datasetsfor use with deep learning applications, multiple datasets created from variousregions under different conditions are needed. To demonstrate a powerful new method for integrating heterogeneous UAV datasets, this paper applies a combined segmentation network (CSN) to share UAVid and semantic drone dataset encoding blocks to learn their general features, whereas its decoding blocks are trained separately on each dataset. Experimental results show that our CSN improves the accuracy of specific classes (e.g., cars), which currently comprise a low ratio in both datasets. From this result, it is expected that the range of UAV dataset utilization will increase.

• Korean Journal of Geomatics
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• v.4 no.2
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• pp.39-45
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• 2004

Building reconstruction attempts to generate geometric and radiometric models of existing buildings usually from sensory data, which have been traditionally aerial or satellite images, more recently airborne LIDAR data, or the combination of these data. Extensive studies on building reconstruction from these data have developed some competitive algorithms with reasonable performance and some degree of automation. Nevertheless, the level of details and completeness of the reconstructed building models often cannot reach the high standards that is now or will be required by various applications in future. Hence, the use of terrestrial sensory data that can provide higher resolution and more complete coverage has been intensively emphasized. We developed a fusion framework for building reconstruction from terrestrial sensory data, that is, points from a laser scanner, images from digital camera, and absolute coordinates from a total station. The proposed approach was then applied to reconstructing a building model from real data sets acquired from a large complex existing building. Based on the experimental results, we assured that the proposed approach cam achieve high resolution and accuracy in building reconstruction. The proposed approach can effectively contribute in developing an operational system producing large urban models for 3D GIS with reasonable resources.