• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.215-220
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• 2004

유역수문모형에서 SCS방법 적용시 입력되는 주요 매개변수인 유역평균 CN값은 RS와 GIS를 적용하면 쉽게 계산이 가능하나, 입력자료의 종류 및 공간해상도의 선택에 따라 같은 자료일 지라도 다르게 계산된다. 본 연구에서는 Landsat 7 ETM+,SPOT 5, 1:25,000 토지이용현황도, 정밀토양도, 개략토양도를 이용하여 2.5m, 30m 공간해상토의 자료로부터 유역평균CN을 계산하였으며, 이들을 각각 2.5m～1000m 크기의 격자로 재배열하여 유역평균을 비교함으로서 해상도가 유역 CN값 변화와 어떠한 관계가 있는지 분석하고자 하였다. 분석결과 해상도에 따라 일정구간 이상에 대하여 오차를 나타내고 있었으며, 자료의 선택에 따라 유역평균 CN 값의 변화가 있는 것으로 분석되었다.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.260-265
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• 2009

최근 전 세계적으로 많이 활용되는 MODIS영상은 250m 밴드 2개, 500m 밴드 5개, 1km 해상도의 밴드 29개로 제공된다. 그러나, 국내의 경우 상대적으로 국토 면적이 작고, 그 구조가 복잡하여 MODIS영상의 1km, 500m의 낮은 공간해 상도는 제약점으로 지적되고 있다. 따라서 본 연구에서는 이러한 공간해상도의 제약점을 해결하기 위해, MODIS 250m 2개 밴드와 500m 5개 밴드 영상을 합성하여 250m 공간해상도의 7개 밴드를 제작하였다. 이를 위해 Wavelet 합성방법을 비롯한 7개 합성방법을 적용하였으며, 6개의 통계적 합성 평가 척도를 적용하여 MODIS 합성 결과를 분석하였다. 연구 결과, 육안평가로는 LMVM 합성기법이 가장 선명한 합성영상을 제시하였으며, 합성평가 척도에서는 각 밴드별로 적합한 합성기법이 다르게 나왔으나, 전체적으로 PCA, LST, LMVM합성방법이 상대적으로 우수한 합성결과를 나타냈었다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.141-156
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• 2023

This study aims to improve understanding of freight movement, crucial for logistics facility investment and policy making. It addresses the limitations of traditional freight truck traffic data, aggregated only at city and county levels, by developing a new methodology. This method uses trip chain data for more detailed, facility-level analysis of freight truck movements. It employs DTG (Digital Tachograph) data to identify individual truck visit locations and creates H3 system-based polygons to represent these visits spatially. The study also involves an algorithm to dynamically determine the optimal spatial resolution of these polygons. Tested nationally, the approach resulted in polygons with 81.26% spatial fit and 14.8% error rate, offering insights into freight characteristics and enabling clustering based on traffic chain characteristics of freight trucks and visited facility types.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.7C
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• pp.654-662
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• 2009

Video streams for mobile video streaming can be encoded to fit the available network bandwidth by controlling three factors: temporal resolution, spatial resolution, and picture quality. The controlling of picture quality by modifying the quantization parameter (QP) is most widely used. In this paper, we demonstrate that reducing the spatial resolution adaptively can be more efficient in terms of picture quality and energy consumption in low bit-rate environment, and present a model to find the optimal spatial resolution for the available bandwidth. Adaptive spatial resolution control scheme is especially effective when the bandwidth between the video server and the mobile device varies considerably with time, and when the mobile device is sensitive to energy consumption. Our scheme can improve the picture quality by approximately O.5dB and reduce energy consumption by more than 50% compared to the conventional video coding in low bit-rate environment.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.1
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• pp.23-29
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• 2014

The emergence of high resolution satellite images and the evolution of spatial resolution facilitate various studies using high resolution satellite images. Above all, target detection algorithms are effective for monitoring of traffic flow and military surveillance and reconnaissance because vehicles, airplanes, and ships on broad area could be detected easily using high resolution satellite images. Recently, many satellites are launched from global countries and the diversity of satellite images are also increased. On the contrary, studies on comparison about the spatial resolution or target detection, especially, are insufficient in domestic and foreign countries. Therefore, in this study, effects of spatial resolution on target detection are analyzed using the PSO target detection algorithm. The resampling techniques such as nearest neighbor, bilinear, and cubic convolution are adopted to resize the original image into 0.5m, 1m, 2m, 4m spatial resolutions. Then, accuracy of target detection is assessed according to not only spatial resolution but also resampling method. As a result of the study, the resolution of 0.5m and nearest neighbor among the resampling methods have the best accuracy. Additionally, it is necessary to satisfy the criteria of 2m and 4m resolution for the detection of airplane and ship, respectively. The detection of airplane need more high spatial resolution than ship because of their complexity of shape. This research suggests the appropriate spatial resolution for the plane and ship target detection and contributes to the criteria of satellite sensor design.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.369-370
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• 2012

프로젝터를 이용하여 대형 화면을 투사할 경우, 밝기와 해상도 등으로 인하여 세세한 시각 정보 전달에 어려움이 발생한다. 본 논문에서는 공간에 설치된 프로젝터가 투영한 낮은 해상도의 대형 화면에서 사용자의 관심 영역을 하이라이트하는 방식으로 이 문제를 해결하고자 한다. 이를 위해 사용자의 관심 영역을 제스쳐 인식을 통해 파악하고, 사용자와 상호작용할 수 있는 지능화 공간과 증강된 시각정보를 투영할 수 있는 프로젝터가 탑재된 모바일 로봇을 소개한다. 본 논문에서는 지능화 공간 내에서 프로젝터가 탑재된 로봇을 이용하여 상호작용이 가능한 다중해상도 디스플레이 방식을 기술하고, 실험을 통하여 이 시스템의 가능성 및 유용성을 확인하고자 한다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2006.05a
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• pp.13-19
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• 2006

본 연구에서는 보다 정확한 식생지수의 분석을 위해서 영상융합에 의한 공간해상도 향상 가능성에 대해서 평가하였다. 식생지수는 광범위한 지역의 산림 모니터링이나 산불재해와 같은 분야에서 폭넓게 사용되고 있으며, 영상융합은 저해상도의 다중분광대 영상을 고해상도의 단일분광대를 이용하여 분광해상도는 그대로 유지하면서 공간해상도를 향상시키는 기법이다. 본 연구에서는 공간해상도 향상을 위해서 wavelet, PCA, IHS, Brovey, multiplicative 변환 등의 영상융합기법을 적용하였으며, 융합된 영상과 원영상으로부터 식생지수를 계산하고 상관계수의 비교를 수행하였다. 그 결과 PCA 방법이 공간해상도 증가와 함께 식생지수의 왜곡이 가장 적은 것으로 나타났다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.11a
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• pp.57-58
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• 2023

우리나라 연안해역에 급증하는 해상풍력단지 등 대규모 해양개발 행위로 인해 무분별한 해역을 점용하게 됨에 따라, 과거 관습적으로 이용해오는 해상교통로와 상충되어 발생하는 사회적 충돌이 문제가 되고 있다. 특히, 선박 대형화 및 자동화, 자율운항선박 등장 등 해상교통환경 변화에 따라 안전한 연안 해상교통환경 조성이 시급한 상황이다. 모든 연안해역의 해상교통 흐름 및 교통밀집도 분석에 따른 관습적 해상교통망을 식별하고, 이를 지정·구축하고자, 빅데이터 및 지리정보시스템(GIS) 기반 해양공간분석기법을 활용하여 검토 및 분석하였다. 이렇게 분석 및 식별된 결과는 DB화하여 디지털 GIS 관리시스템과 연계하도록 추진중이다.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.1
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• pp.40-48
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• 2011

The purpose of this study is to contribute enhancing the accuracy of viewshed analysis through the explanation for an analysis method of viewshed analysis using GIS. According to previous studies, the visible area using digital terrain in viewshed analysis depends on a visible interest area, scale of terrain, spatial resolution and surface data. In this study, we used trend analysis and RMSE analysis in order to find the effect of a visible interest area, scale of terrain, etc in viewshed analysis. Results of this study are as follows. First, the result of viewshed analysis depends on a visible interest area, scale of terrain, spatial resolution, surface data such as previous studies. Second, the results in forest area are reliable than those of flat area in terms of a visible interest area. Third, the results based on raster grid data are stable than those of TIN(triangulated irregular network) in terms of input surface data. Fourth, according to the result of trend and RMSE analysis, the spatial resolution for analysis is differently applied to different scales digital terrain map in viewshed analysis. In detail, it is desirable that the spatial resolution is set less than 10m(in the case of 1/1,000 digital terrain map), 20m(in the case of 1/5,000 map), 30m(1/25,000 map).

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1573-1587
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• 2021

A Drone image is an ultra-high-resolution image that is several or tens of times higher in spatial resolution than a satellite or aerial image. Therefore, drone image-based remote sensing is different from traditional remote sensing in terms of the level of object to be extracted from the image and the amount of data to be processed. In addition, the optimal scale and size of data used for model training is different depending on the characteristics of the applied deep learning model. However, moststudies do not consider the size of the object to be found in the image, the spatial resolution of the image that reflects the scale, and in many cases, the data specification used in the model is applied as it is before. In this study, the effect ofspatial resolution and image size of drone image on the accuracy and training time of the semantic segmentation deep learning model of six wintering vegetables was quantitatively analyzed through experiments. As a result of the experiment, it was found that the average accuracy of dividing six wintering vegetablesincreases asthe spatial resolution increases, but the increase rate and convergence section are different for each crop, and there is a big difference in accuracy and time depending on the size of the image at the same resolution. In particular, it wasfound that the optimal resolution and image size were different from each crop. The research results can be utilized as data for getting the efficiency of drone images acquisition and production of training data when developing a winter vegetable segmentation model using drone images.