• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.804-811
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• 2010

The lower portion of sloping paddy fields normally contains excessive moisture and the higher water table caused by the inflow of ground water from the upper part of the field resulting in non-uniform water content distribution. Four drainage methods namely Open Ditch, Vinyl Barrier, Pipe Drainage and Tube Bundle for multiple land use were installed within 1-m position from the lower edge of the upper embankment of sloping alluvial paddy fields. Knowledge of the spatial variability of soil water properties is of primary importance for management of agricultural lands. This study was conducted to evaluate the effect of drainage in the soil on spatial variability of soil water content using the geostatistical analysis. The soil water content was collected by a TDR (Time Domain Reflectometry) sensor after the installation of subsurface drainage on regular square grid of 80 m at 20 m paddy field located at Oesan-ri, Buk-myeon, Changwon-si in alluvial slopping paddy fields ($35^{\circ}22^{\prime}$ N, $128^{\circ}35^{\prime}$). In order to obtain the most accurate field information, the sampling grid was divided 3 m by 3 m unit mesh by four drainage types. The results showed that spatial variance of soil water content by subsurface drainage was reduced, though yield of soybean showed the same trends. Value of "sill" of soil water content with semivariogram was 9.7 in Pipe Drainage, 86.2 in Open Ditch, and 66.8 in Vinyl Barrier and 15.7 in Tube Bundle.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.1-12
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• 2005

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. This paper presents a new algorithm for robust registration of the images acquired by multiple sensors having different modalities; the EO (electro-optic) and IR(infrared) ones in the paper. The two feature-based and intensity-based approaches are usually possible for image registration. In the former selection of accurate common features is crucial for high performance, but features in the EO image are often not the same as those in the R image. Hence, this approach is inadequate to register the E0/IR images. In the latter normalized mutual Information (nHr) has been widely used as a similarity measure due to its high accuracy and robustness, and NMI-based image registration methods assume that statistical correlation between two images should be global. Unfortunately, since we find out that EO and IR images don't often satisfy this assumption, registration accuracy is not high enough to apply to some applications. In this paper, we propose a two-stage NMI-based registration method based on the analysis of statistical correlation between E0/1R images. In the first stage, for robust registration, we propose two preprocessing schemes: extraction of statistically correlated regions (ESCR) and enhancement of statistical correlation by filtering (ESCF). For each image, ESCR automatically extracts the regions that are highly correlated to the corresponding regions in the other image. And ESCF adaptively filters out each image to enhance statistical correlation between them. In the second stage, two output images are registered by using NMI-based algorithm. The proposed method provides prospective results for various E0/1R sensor image pairs in terms of accuracy, robustness, and speed.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.127-133
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• 2014

The purpose of this study is to evaluate the accuracy of Network-RTK(VRS) survey for applying to Ground Control Points(GCPs) survey required for mapping aerial photographs. Network-RTK has been serviced by National Geographic Information Institute since 2007. On the basis of the global coordinates system(ITRF2000), the coordinates of GCPs determined by Static GNSS survey with relative positioning techniques were regarded as accurate values. The coordinates of GCPs were also determined by Network-RTK survey using two kinds of receivers, and then they were converted into the global coordinates system(ITRF2000) by applying suitable geoid model and coordinate transformation. These coordinates of GCPs were compared with those from Static GNSS survey. The root mean squares error (RMSE) of coordinate differences between Network-RTK and Static GNSS was ${\pm}2.0cm$ in plane and ${\pm}7.0cm$ in height. Therefore, Network-RTK survey that enables single GNSS receiver to measure positions in short time is a practical alternative in positioning GCPs to either RTK survey that uses more than two sets of GNSS receivers or Static GNSS survey that requires longer observation time.

• Journal of the Korean Geographical Society
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• v.39 no.5 s.104
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• pp.786-803
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• 2004

These days, interests on land cover classification using not only multi-sensor data but also thematic GIS information, are increasing. Often, although we have useful GIS information for the classification, the traditional classification method like maximum likelihood estimation technique (MLE) does not allow us to use the information due to the fact that the MLE and the existing computer programs cannot handle GIS data properly. We proposed a new method for updating the image classification using multi-spectral and multi-temporal images. In this study, we have simultaneously extended the MLE to accommodate both multi-spectral images data and land cover data for land cover classification. In addition to the extended MLE method, we also have extended the empirical likelihood ratio estimation technique (LRE), which is one of non-parametric techniques, to handle simultaneously both multi-spectral images data and land cover data. The proposed procedures were evaluated using land cover map based on Landsat ETM+ images in the Anmyeon-do area in South Korea. As a result, the proposed methods showed considerable improvements in classification accuracy when compared with other single-spectral data. Improved classification images showed that the overall accuracy indicated an improvement in classification accuracy of $6.2\%$ when using MLE, and $9.2\%$ for the LRE, respectively. The case study also showed that the proposed methods enable the extraction of the area with land cover change. In conclusion, land cover classification produced through the combination of various GIS spatial data and multi-spectral images will be useful to involve complementary data to make more accurate decisions.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.99-105
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• 2013

In rapidly developing urban areas that include high-rise, large, and complex buildings, indoor and outdoor maps in GIS become a basis for utilizing and sharing information pertaining to various aspects of the real world. Although an indoor mapping has gained much attentions, research efforts are mostly in 2D and 3D modeling of terrain and buildings. Therefore, to facilitate fast and accurate construction of indoor GIS, this paper proposes a semi-automatic method consisting of preprocessing, 2D mapping, and 3D mapping stages. The preprocessing is designed to estimate heights of building interiors and to identify noise data from point clouds. In the 2D mapping, a floor map is extracted with a tracing grid and a refinement method. In the 3D mapping, a 3D wireframe model is created with heights from the preprocessing stage. 3D mesh data converted from noise data is combined with the 3D wireframe model for detail modeling. The proposed method was applied to point clouds depicting a hallway in a building. Experiment results indicate that the proposed method can be utilized to construct 2D and 3D maps for indoor GIS.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.27-34
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• 2013

Due to the increased interest in global warming, interest in forest resources aimed towards reducing greenhouse gases have subsequently increased. Thus far, data related to forest resources have been obtained, through the employment of aerial photographs or satellite images, by means of plotting. However, the use of imaging data is disadvantageous; merely, due to the fact that recorded measurements such as the height of trees, in dense forest areas, lack accuracy. Within such context, the authors of this study have presented a method of data processing in which an individual tree is isolated within forested areas through the use of LIDAR data and ortho-images. Such isolation resulted in the provision of more efficient and accurate data in regards to the height of trees. As for the data processing of LIDAR, the authors have generated a normalized digital surface model to extract tree points via local maxima filtering, and have additionally, with motives to extract forest areas, applied object oriented image classifications to the processing of data using ortho-images. The final tree point was then given a figure derived from the combination of LIDAR and ortho-images results. Based from an experiment conducted in the Yongin area, the authors have analyzed the merits and demerits of methods that either employ LIDAR data or ortho-images and have thereby obtained information of individual trees within forested areas by combining the two data; thus verifying the efficiency of the above presented method.

• Journal of the Korean association of regional geographers
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• v.3 no.2
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• pp.1-17
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• 1997

Geographic Information Systems(GIS) are very useful for spatial analysis and policy in local government administration. Recognizing the value of GIS, Chungnam province authorities put a spur on the introduction and development of it. But they have some difficulty in this process because of technical restraint, expertise shortage and budget limit. This study has surveyed current achievement and conditions for GIS development and presented general framework and subordinate tasks to build up GIS. First of all, there are a few prior conditions to guarantee the success of GIS: First, we should set up reasonable long-term plan and follow systematic procedures according to the plan. Second, it is essential to clarify what initiatively manage to whole business and so we should make up GIS-Board as an institutional center for this job. Third, we must research how to take advantage of already existing NGIS(National Geographic Information System), so that we may eliminate redundancy of investment. We can save a lot of finance and human resources through it. Fourth, we must focus on the importance of accurate mapping by utilizing new technology like GPS(Global Positioning System). Fifth, we should arrange efficient training program to constantly produce excellent human resources for GIS.

• Journal of Korean Society for Geospatial Information Science
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• v.1 no.1 s.1
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• pp.193-201
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• 1993

In a number of areas, there are important benefits to be gained when we bring both the detection and monitoring abilities of remote sensing as well as the philosophical approach and analytic capabilities of a geographic information system to bear on a problem. A key area in the joint applications of remote sensing technology and GIS is to identify change. Whether this change is of interest for its own sake, or because the change causes us to act (for example, to update a map), remote sensing provides an excellent suite of tools for detecting change. At the same time, a GIS is perhaps the best analytic toot for quantifying the process of change. There are two alternative methods for quantifying changes. The conceptually simple approach is to un the pixel values in each of the images. This method is practical but may be too simple to identify the variety of changes in a complex scene. The common alternative is called symbolic change detection. The analyst first decides on a set of thematic categories that are important to distinguish for the application. This approach is useful only if accurate landuse/cover classifications can be obtained. Persons conducting digital change detection must be intimately familiar with the environment under study, the quality of the data set and the characteristics of change detection algorithms. Also, much work remains to identify optimum change detection algorithms for specific geographic areas and problems.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.173-181
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• 2017

In this paper, we have studied tracking as a training stage of considering the position and the scale of a person given its previous position, scale, as well as next and forward image fraction. Unlike other learning methods, CNN is thereby learning combines both time and spatial features from the image for the two consecutive frames. We introduce multiple path ways in CNN to better fuse local and global information. A creative shift-variant CNN architecture is designed so as to alleviate the drift problem when the distracting objects are similar to the target in cluttered environment. Furthermore, we employ CNNs to estimate the scale through the accurate localization of some key points. These techniques are object-independent so that the proposed method can be applied to track other types of object. The capability of the tracker of handling complex situations is demonstrated in many testing sequences. The accuracy of the SVM classifier using the features learnt by the CNN is equivalent to the accuracy of the CNN. This fact confirms the importance of automatically optimized features. However, the computation time for the classification of a person using the convolutional neural network classifier is less than approximately 1/40 of the SVM computation time, regardless of the type of the used features.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.81-87
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• 2016

It is important to analyze the exact damage information for fast recovery when natural disasters cause damage on river-side facilities such as dams, bridges, embankments etc. In this study, we shows the method to effectively damage analysis plan using UAV(Unmanned aerial vehicle) images and accuracy assessment of it. The UAV images are captured on area near the river-side facilities and the core methodology for damage analysis are image matching and change detection algorithm. The result(point cloud) from image matching is to construct 3-dimensional data using by 2-dimensional images, it extracts damage areas by comparing the height values on same area with reference data. The results are tested absolute locational precision compared by post-processed aerial LiDAR data named reference data. The assessment analysis test shows our matching results 10-20 centimeter level precision if external orientation parameters are very accurate. This study shows suggested method is very useful for damage analysis in a large size structure like river-side facilities. But the complexity building can't apply this method, it need to the other method for damage analysis.