• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.107-112
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• 2011

Several developed countries have been developing their own satellite navigation systems, such as Europe's Galileo, China's BEIDOU, and Japan's QZSS, to cope with clock errors and signal vulnerabilities of GPS. In addition, modernization of Loran, eLoran, for GPS backup has been conducted. In Korea, a dependent navigation system has been required and for GPS backup, the need for utilization of time synchronization infrastructure through the modernization of Loran has been raised. Loran signal uses 100Khz groundwave. A significant factor limiting the ranging accuracy of the Loran signal is the ASF arising from the fact that the groundwave signal is likely to propagate over paths of varying conductivity and topography. Thus, an ASF compensation method is very important for Loran and eLoran navigation. This paper introduces the propagation delay model and then compares and analyzes the estimations from the propagation delay model and measured ASFs.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.3
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• pp.88-100
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• 2009

An inundation analysis was performed on Hwapocheon, one of the tributaries of Nakdong River, which was inundated by heavy rain in August, 2002 with overtopping and levee break. The results of the developed model, 2D diffusion wave inundation analysis model, was compared with inundation trace map as well as inundation depth in terms of time and maximum inundated area calculated from FLUMEN model for the assessment of model applicability. The results from the developed model showed high fitness of 88.61% in comparison with observed data. Also maximum inundated area and spatial distribution of inundation zone were also found to be consistent with the results of FLUMEN model. Therefore, inundation zone and maximum inundation area calculated over a period of time by adopting 2D diffusion wave inundation analysis model can be used as a database for identifying high risk areas of inundation and establishing flood damage reduction measures.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.29-38
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• 2010

It is necessary to develop an integration model which can account for various data acquired at different measurement scales in environmental thematic mapping with high-resolution ground survey data and relatively low-resolution remote sensing data. This paper presents and applies a multi-scale geostatistical methodology for downscaling of thematic maps generated from lowresolution remote sensing data. This methodology extends a traditional ordinary kriging system to a block kriging system which can account for both ground data and remote sensing data which can be regarded as point and block data, respectively. In addition, stochastic simulation based on block kriging is also applied to describe spatial uncertainty attached to the downscaling. Two downscaling experiments including SRTM DEM and MODIS Leaf Area Index (LAI) products were carried out to illustrate the applicability of the geostatistical methodology. Through the experiments, multi-scale geostatistics based on block kriging successfully generated relatively high-resolution thematic maps with reliable accuracy. Especially, it is expected that multiple realizations generated from simulation would be effectively used as input data for investigating the effects of uncertain input data on GIS model outputs.

• Journal of The Geomorphological Association of Korea
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• v.24 no.3
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• pp.105-118
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• 2017

The debris slope landforms which are existent in Korean mountains is generally on the steep slopes and mostly covered by vegetation, it is difficult to investigate the landform. Therefore a scientific method is required to come up with an effective field investigation plan. For this purpose, the use of Remote Sensing and GIS technologies for a spatial analysis is essential. This study has extracted the potential area of debrisslope landform formation using Fuzzy set and Bayesian Predictive Discriminate Model as mathematical data integration methods. The first step was to obtain information about debris locations and their related factors. This information was verified through field investigation and then used to build a database. In the second step, the map that zoning the study area based on the degree of debris formation possibility was generated using two modeling methods, and then cross validation technique was applied. In order to quantitatively analyze the accuracy of two modeling methods, the calculated potential rate of debrisformation within the study area was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). As a result, the prediction accuracy of Fuzzy set model wes 83.1% and Bayesian Predictive Discriminate Model wes 84.9%. It showed that two models are accurate and reliable and can contribute to efficient field investigation and debris landform management.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.1
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• pp.37-45
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• 2009

Susceptibility assessment of groundwater contamination is a useful tool for many aspects of regional and local groundwater resources planning and management. It can be used to direct regulatory, monitoring, educational, and policy-making efforts to highly vulnerable areas. In this study, a semi process-based was proposed to evaluate relative susceptibilities to groundwater contamination by nitrate on a regional scale. Numerical simulation based on data from each soil series was done to model water flow within soil profiles that were related to groundwater contamination by nitrate. Relative vulnerability indices for each soil series were produced by manipulation of amount of leaching flux, amount of average water storage in a soil profile, and amount of average water storage change. These indices were designed to convey the trend of leaching flux and to maximize spatial resolution. The resulting vulnerability distribution map was used to locate highly vulnerable sites easily with an appropriate grouping the indices, and was then compared with those from groundwater nitrate concentrations monitored. An excellent agreement was obtained across nitrate concentrations from the highly vulnerable regions and those from the low to stable regions.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.4
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• pp.219-228
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• 2021

During our observations of changes in halophyte distribution in Hampyeong Bay over a period of five years, we found that the distribution area showed a maintenance for Phragmites communis community, a tendency of gradual increase for Zoysia sinica community, gradual decrease for Suaeda maritima community, and disappearance for Limonium tetragonum community during the studied period. The Phragmites communis community stably settled in areas adjacent to land and appeared not to be significantly affected by physical factors (such as tides and waves) or disturbances caused by biological factors (such as interspecific competition). Among studied species, germination time was shown to be the fastest for Suaeda maritima. In addition, this species showed certain characteristics that allowed it to settle primarily in new habitats formed by sand deposition as its growth was not halted under conditions with high amounts of sand and high organic matter content. However, in areas where Zoysia sinica and Suaeda maritima resided together, the area inhabited by Suaeda maritima gradually decreased due to interspecific competition between the two species. This was believed to be the result of a sharp decrease in the germination of Suaeda maritima since May, while the germination of Zoysia sinica was continuously maintained, indicating that the latter had an advantage in terms of seedling competition. In the case of the Limonium tetragonum community, its habitat was found to have been completely destroyed because it was covered by sand. The study area was confirmed to have undergone a large change in topography as tides and waves resulted in sand deposition onto these lands. Hampyeong Bay is considered to have experienced changes in halophyte distribution related to certain complex factors, such as changes in physical habitats and changes in biological factors such as interspecific competition.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1061-1070
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• 2021

In this manuscript, we tried to improve the performance of the FC-DenseNet by applying an attention gate for the classification of cropping areas. The attention gate module could facilitate the learning of a deep learning model and improve the performance of the model by injecting of spatial/spectral weights to each feature map. Crop classification was performed in the onion and garlic regions using a proposed deep learning model in which an attention gate was added to the skip connection part of FC-DenseNet. Training data was produced using various PlanetScope satellite imagery, and preprocessing was applied to minimize the problem of imbalanced training dataset. As a result of the crop classification, it was verified that the proposed deep learning model can more effectively classify the onion and garlic regions than existing FC-DenseNet algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.483-497
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• 2018

Image mosaicking, which combines several images into one image, is effective for analyzing images and important in various fields of spatial information such as a continuous image map. The crucial processes of the image mosaicking are optimal seamline determination and color correction of mosaicked images. In this study, the overlap regions were determined by SURF (Speeded Up Robust Features) for image matching. Based on the characteristics of the edges extracted by Canny filter, seamline candidates were selected from classified edges with their characteristics, and the edges were connected by using Dijkstra algorithm. In particular, anisotropic filter and image pyramid were applied to extract reliable seamlines. In addition, it was possible to determine seamlines effectively and efficiently by utilizing building and road layers from digital maps. Finally, histogram matching and seamline feathering were performed to improve visual quality of the mosaicked images.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.125-131
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• 2019

A vanishing point is a point on an image to which parallel lines projected from a real space gather. A vanishing point in a road space provides important spatial information. It is possible to improve the position of an extracted lane or generate a depth map image using a vanishing point in the road space. In this paper, we propose a method of detecting vanishing points on images taken from a vehicle's point of view using Deep Neural Network (DNN) and Histogram of Oriented Gradient (HoG). The proposed algorithm is divided into a HoG feature extraction step, in which the edge direction is extracted by dividing an image into blocks, a DNN learning step, and a test step. In the learning stage, learning is performed using 2,300 road images taken from a vehicle's point of views. In the test phase, the efficiency of the proposed algorithm using the Normalized Euclidean Distance (NormDist) method is measured.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.