• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1975-1984
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• 2021

Urban expansion results in raising the temperature in the city, which can cause social, economic and physical damage. In order to prevent the urban heat island and reduce the urban land surface temperature, it is important to quantify the cooling effect of the features of the urban space. Therefore, in order to understand the relationship between each object of land cover and the land surface temperature in Seoul, the land cover map was classified into 6 classes. And the correlation and multiple regression analysis between land surface temperature and the area of objects, perimeter/area, and normalized difference vegetation index was analyzed. As a result of the analysis, the normalized difference vegetation index showed a high correlation with the land surface temperature. Also, in multiple regression analysis, the normalized difference vegetation index exerted a higher influence on the land surface temperature prediction than other coefficients. However, the explanatory power of the derived models as a result of multiple regression analysis was low. In the future, if continuous monitoring is performed using high-resolution MIR Image from KOMPSAT-3A, it will be possible to improve the explanatory power of the model. By utilizing the relationship between such various land cover types considering vegetation vitality of green areas with that of land surface temperature within urban spaces for urban planning, it is expected to contribute in reducing the land surface temperature in urban spaces.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.939-944
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• 2002

In this paper, we propose and apply new classification method to the remotely sensed image acquired from airborne multi-spectral scanner. This is a neuro-fuzzy image classifier derived from the generic model of a 3-layer fuzzy perceptron. We implement a classification software system with the proposed method for land cover image classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. The results show that the neuro-fuzzy classification method classifies more accurately than the maximum likelihood method. In comparing the maximum-likelihood classification map with the neuro-fuzzy classification map, it is apparent that there is more different as amount as 7.96% in the overall accuracy. Most of the differences are in the "Building" and "Pine tree", for which the neuro-fuzzy classifier was considerably more accurate. However, the "Bare soil" is classified more correctly with the maximum-likelihood classifier rather than the neuro-fuzzy classifier.

• Spatial Information Research
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• v.11 no.4
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• pp.509-518
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• 2003

Impervious surface affects urban climate, flood, and water pollution. With a higher paved rate, expanded heat containing capacity of buildings and roads raises atmospheric temperature, and increased quantity of the outflowed water brings flood during a heavy downpour. Moreover, increased non-point source pollutant load is accountable for water pollution. In this regard, it is definitely important to research and keep monitoring the current situation of paved surface, which influences urban ecosystem, disaster and pollution. In fact, collecting information on urban paved surface, which requires the time and expense, is very difficult due to its complicate structure. In order to solve the problem, this study suggested a method to utilize satellite image data for efficient survey on the current condition of paved surface. It analyzed the paved surface condition of Anyang-si by using IKONOS image and discussed the usefulness and limitation of this method.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1497-1503
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• 2023

To address challenges in classifying clouds and snow cover when calculating ground reflectance in Near-UltraViolet (UV) wavelengths, this study introduces a methodology that combines cloud data from the Geostationary Environmental Monitoring Spectrometer (GEMS) and the Advanced Meteorological Imager (AMI)satellites for snow cover analysis. The proposed approach aims to enhance the quality of surface reflectance calculations, and combined cloud data were generated by integrating GEMS cloud data with AMI cloud detection data. When applied to compute GEMS surface reflectance, this fusion approach significantly mitigated underestimation issues compared to using only GEMS cloud data in snow-covered regions, resulting in an approximately 17% improvement across the entire observational area. The findings of this study highlight the potential to address persistent underestimation challenges in snow areas by employing fused cloud data, consequently enhancing the accuracy of other Level-2 products based on improved surface reflectivity.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2006.05a
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• pp.169-174
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• 2006

도시지역의 급변하는 토지이용의 패턴 및 토지피복상태 등의 도시환경의 변화를 분석하는 것은 도시계획 및 개발계획을 기획, 입안하는데 중요한 자료로 활용될 수 있을 것이다. 본 연구에서는 구미시를 대상지역으로 하는 Landsat TM과 Landsat $ETM^+$ 인공위성 영상데이터로부터 토지피복/토지이용 분류를 수행함으로써 18년간의 광역적 도시변화를 탐지하였다. 또한 도시의 발전과 지표면 온도의 상관성을 알아보기 위하여 열적외선 파장영역을 이용하여 온도를 추출하였다. 시가지 확장으로 인한 지표면 온도의 상승을 확인하고 이를 통해 토지이용/토지피복의 상관관계 분석 및 식생지수분포를 비교하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.7 no.1
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• pp.49-57
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• 1989

On this study, we performed the landuse classification using the Landsat data acquired before and after reclamation, and extracted the ground temperature from infrared band(TM band6) data. Using the satellite data, it was possible to extract changes of landuses effectively according to the reclamation, and could obtain the thermal characteristics of the reclaimed area and the surroundings by converting infrared data value into temperatures of surfaces of ground and water. The result of this analysis will be used for the land management of large-scale reclaimed area applying the satellite data and related information.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.33-40
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• 2005

The aim of remotely sensed data classification is to produce the best accuracy map of the earth surface assigning each pixel to its appropriate category of the real-world. The classification of satellite multi-spectral image data has become tool for generating ground cover map. Many classification methods exist. In this study, MLC(Maximum Likelihood Classification), ANN(Artificial neural network), SVM(Support Vector Machine), Naive Bayes classifier algorithms are compared using IKONOS image of the part of Dalsung Gun, Daegu area. Two preprocessing methods are performed-PCA(Principal component analysis), ICA(Independent Component Analysis). Boosting algorithms also performed. By the combination of appropriate feature selection pre-processing and classifier, the best results were obtained.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.55-59
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• 2008

For effective mobility control of UGV(unmanned ground vehicle), the terrain cover classification is an important component as well as terrain geometry recognition and obstacle detection. The vision based terrain cover classification algorithm consists of pre-processing, feature extraction, classification and post-processing. In this paper, we present a method to classify terrain covers based on the color and texture information. The color space conversion is performed for the pre-processing, the wavelet transform is applied for feature extraction, and the SVM(support vector machine) is applied for the classifier. Experimental results show that the proposed algorithm has a promising classification performance.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.407-408
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• 2019

토지 피복과 관련된 지표면 파라미터는 일반적으로 지표에서 감지되어 위성영상에 나타난 많은 물리적 프로세스에 의존하며 계절적 주기성을 갖는 시간적 변화를 보인다. 하모닉 모형은 복잡한 파형을 정현파 성분의 합으로 표시함으로써 레벨, 주기, 진폭 및 위상 요소를 통한 변동을 분석함으로써 표면에서 관찰되는 계절적 변화 패턴을 모델링하는 데 적합한 모형이다. 본 연구에서는 MODIS NDVI (Normalized Difference Vegetation Index) 시계열 자료를 이용하여 하모닉 패턴의 특성에 따라 토지 피복을 분류하는 방법론을 제안하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.41-48
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• 2024

In 2017, the Ministry of the Interior and Safety conducted research for the revision of seismic design standards and performed studies on standard design response spectra. As a result, the Common Application Guidelines for Seismic Design Standards were introduced, and these guidelines have been implemented in the national design standards of the Ministry of Land, Infrastructure, and Transport for practical use. However, it should be noted that the research for proposing standard design response spectra during the 2017 revision was conducted before the occurrence of the significant seismic events in South Korea, such as the 2016 Gyeongju Earthquake and the 2017 Pohang Earthquake. To account for these recent major earthquakes, this study adjusted the standard design spectra based on the records of the 2016 Gyeongju Earthquake and the 2017 Pohang Earthquake and conducted ground response analyses accordingly. The results revealed variations in peak ground acceleration (PGA) at the ground surface even within the same ground classification. It was confirmed that this variation can lead to overestimation or underestimation of seismic loads.