• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.59-68
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• 2011

The analysis of optimal infiltration path is one of the representative fields in which the GIS technology can be useful for the military purpose. Usually the analysis of the optimal path is done with network data. However, for military purpose, it often needs to be done with raster data. Because raster data needs far more computation than network data, it is difficult to apply the methods usually used in network data, such as Dijkstra algorithm. The genetic algorithm, which has shown great outcomes in optimization problems, was applied. It was used to minimize the detection probability of infiltration route. 2D binary array genes and its crossover and mutation were suggested to solve this problem with raster data. 30 tests were performed for each population size, 500, 1000, 2000, and 3000. With each generation, more adoptable routes survived and made their children routes. Results indicate that as the generations increased, average detection probability decreased and the routes converged to the optimal path. Also, as the population size increases, more optimal routes were found. The suggested genetic algorithm successfully finds the optimal infiltration route, and it shows better performance with larger population.

• Economic and Environmental Geology
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• v.41 no.6
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• pp.747-761
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• 2008

In order to experiment the acoustic remote classification of seabed sediment, we achieved ground-truth data(i.e. video and grab samples, etc.) and developed post-processing for automatic classification procedure on the basis of 300 kHz MultiBeam Echo Sounder(MBES) backscattering data, which was acquired using KONGBERG Simrad EM3000 at Sock-Cho Port, East Sea of South Korea. Sonar signal and its classification performance were identified with geo-referenced video imagery with the aid of GIS (Geographic Information System). The depth range of research site was from 5 m to 22.7 m, and the backscattering amplitude showed from -36dB to -15dB. The mean grain sizes of sediment from equi-distanced sampling site(50 m interval) varied from 2.86$(\phi)$ to 0.88(\phi). To acquire the main feature for the seabed classification from backscattering amplitude of MBES, we evaluated the correlation factors between the backscattering amplitude and properties of sediment samples. The performance of seabed remote classification proposed was evaluated with comparing the correlation of human expert segmentation to automatic algorithm results. The cross-model perception error ratio on automatic classification algorithm shows 8.95% at rocky bottoms, and 2.06% at the area representing low mean grain size.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1643-1652
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• 2020

To investigate the characteristics of detailed flows in a building-congested district, we coupled a computation fluid dynamics (CFD) model to the local data assimilation and prediction system (LDAPS), a current operational numerical weather prediction model of the Korea Meteorological Administration. For realistic numerical simulations, we used the meteorological variables such as wind speeds and directions and potential temperatures predicted by LDAPS as the initial and boundary conditions of the CFD model. We trilinearly interpolated the horizontal wind components of LDAPS to provide the initial and boudnary wind velocities to the CFD model. The trilinearly interpolated potential temperatures of LDAPS is converted to temperatures at each grid point of the CFD model. We linearly interpolated the horizontal wind components of LDAPS to provide the initial and boundary wind velocities to the CFD model. The linearly interpolated potential temperatures of LDAPS are converted to temperatures at each grid point of the CFD model. We validated the simulated wind speeds and directions against those measured at the PKNU-SONIC station. The LDAPS-CFD model reproduced similar wind directions and wind speeds measured at the PKNU-SONIC station. At 07 LST on 22 June 2020, the inflow was east-north-easterly. Flow distortion by buildings resulted in the east-south-easterly at the PKNU-SONIC station, which was the similar wind direction to the measured one. At 19 LST when the inflow was southeasterly, the LDAPS-CFD model simulated southeasterly (similar to the measured wind direction) at the PKNU-SONIC station.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.72.2-72.2
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• 2011

세계전파통신회의 (WRC; World Radiocommunication Conference)회의는 국제전기통신연합(ITU)에서 발행하는 국제 전파법과 관련된 전파규약을 갱신하거나 새로운 법 제정을 위해, 3-4년 간격으로 개최되는 전파통신 관련 회의라고 할 수 있다. 차기 WRC회의는 2012년 1월23일 - 2월 17일에 개최되며, 따라서 동 회의에서 다룰 의제들의 사전 연구결과에 대한 보고서가 필요하다고 하겠다. 그러므로 WRC 본회의 개최 6개월 전에 상기 기술문서를 의제별로 종합 작성하기 위해 CPM (Conference Preperatory Meeting) 회의를 개최해야 하며, 본 CPM회의에서 작성된 기술보고서의 내용들은 WRC 본회의에서의 여러 가지 국제법의 결정사안에 대한 중요한 정보를 제공하게 된다고 할 수 있다. 2011년 2월 14일-2월 25일에 걸쳐 스위스에서 개최된 CPM-11 2차회의에서는 전파천문업무 보호 및 22 GHz 대역 달탐사, 38 GHz 대역 Space-VLBI운용대역과 관련된 WRC-11 의제 1.6, 1.11, 1.12 등에 대해 활발한 토론이 있었다. 따라서 한국천문연구원에서는 향후 일본과 국제공동관측을 수행하게될 space-VLBI의 운용과 관련하여, 관련 과학업무의 보호를 위한 동 회의의 최종결과 및 향후 WRC-12회의에 대한 대응책에 대해서도 알아보고자 한다.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.73.2-73.2
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• 2011

세계전파통신회의 (WRC; World Radiocommunication Conference)회의는 국제전기통신연합(ITU)에서 발행하는 국제 전파법과 관련된 전파규약을 갱신하거나 새로운 법 제정을 위해, 3-4년 간격으로 개최되는 전파통신 관련 회의라고 할 수 있다. 차기 WRC회의는 2012년 1월23일 - 2월 17일에 개최되며, 따라서 동 회의에서 다룰 의제들의 사전 연구결과에 대한 보고서가 필요하다고 하겠다. 그러므로 WRC 본회의 개최 6개월 전에 상기 기술문서를 의제별로 종합 작성하기 위해 CPM (Conference Preperatory Meeting) 회의를 개최해야 하며, 본 CPM회의에서 작성된 기술보고서의 내용들은 WRC 본회의에서의 여러 가지 국제법의 결정사안에 대한 중요한 정보를 제공하게 된다고 할 수 있다. 2011년 2월 14일-2월 25일에 걸쳐 스위스에서 개최된 CPM-11 2차회의에서는 전파천문업무 보호 및 22 GHz 대역 달탐사, 38 GHz 대역 Space-VLBI운용대역과 관련된 WRC-11 의제 1.6, 1.11, 1.12 등에 대해 활발한 토론이 있었다. 따라서 한국천문연구원에서는 전파천문업무와 관련된 국가기고서를 제출하였으며, 이와 관련된 동 회의의 최종결과 및 향후 WRC-12회의에 대한 대응책에 대해서도 알아보고자 한다.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1029-1042
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• 2021

As part of the next-generation Compact Advanced Satellite 500 (CAS500) project, CAS500-4 is scheduled to be launched in 2025 focusing on the remote sensing of agriculture and forestry. To obtain quantitative information on vegetation from satellite images, it is necessary to acquire surface reflectance through atmospheric correction. Thus, it is essential to develop an atmospheric correction method suitable for CAS500-4. Since the absorption and scattering characteristics in the atmosphere vary depending on the wavelength, it is needed to analyze the sensitivity of atmospheric correction parameters such as aerosol optical depth (AOD) and water vapor (WV) considering the wavelengths of CAS500-4. In addition, as CAS500-4 has only five channels (blue, green, red, red edge, and near-infrared), making it difficult to directly calculate key parameters for atmospheric correction, external parameter data should be used. Therefore, thisstudy performed a sensitivity analysis of the key parameters (AOD, WV, and O₃) using the simulated images based on Sentinel-2 satellite data, which has similar wavelength specifications to CAS500-4, and examined the possibility of using the products of GEO-KOMPSAT-2A (GK2A) as atmospheric parameters. The sensitivity analysisshowed that AOD wasthe most important parameter with greater sensitivity in visible channels than in the near-infrared region. In particular, since AOD change of 20% causes about a 100% error rate in the blue channel surface reflectance in forests, a highly reliable AOD is needed to obtain accurate surface reflectance. The atmospherically corrected surface reflectance based on the GK2A AOD and WV was compared with the Sentinel-2 L2A reflectance data through the separability index of the known land cover pixels. The result showed that two corrected surface reflectance had similar Seperability index (SI) values, the atmospheric corrected surface reflectance based on the GK2A AOD showed higher SI than the Sentinel-2 L2A reflectance data in short-wavelength channels. Thus, it is judged that the parameters provided by GK2A can be fully utilized for atmospheric correction of the CAS500-4. The research findings will provide a basis for atmospheric correction of the CAS500-4 in the future.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1047-1060
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• 2017

In recent years, global warming has been continuing and abnormal weather phenomena are occurring frequently. Especially in the 21st century, the intensity and frequency of hydrological disasters are increasing due to the regional trend of water. Since the damage caused by disasters in urban areas is likely to be extreme, it is necessary to prepare a landslide susceptibility maps to predict and prepare the future damage. Therefore, in this study, we analyzed the landslide vulnerability using the logistic model and assessed the management plan after the landslide through the field survey. The landslide area was extracted from aerial photographs and interpretation of the field survey data at the time of the landslides by local government. Landslide-related factors were extracted topographical maps generated from aerial photographs and forest map. Logistic regression (LR) model has been used to identify areas where landslides are likely to occur in geographic information systems (GIS). A landslide susceptibility map was constructed by applying a LR model to a spatial database constructed through a total of 13 factors affecting landslides. The validation accuracy of 77.79% was derived by using the receiver operating characteristic (ROC) curve for the logistic model. In addition, a field investigation was performed to validate how landslides were managed after the landslide. The results of this study can provide a scientific basis for urban governments for policy recommendations on urban landslide management.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.189-199
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• 2017

Thisstudy quantitatively analyzed the research trendsin the use ofICT ofthe environmental field using the text mining technique. To that end, the study collected 359 papers published in the past two decades(1996-2015)from the National Digital Science Library (NDSL) using 38 environment-related keywords and 16 ICT-related keywords. It processed the natural languages of the environment and ICT fields in the papers and reorganized the classification system into the unit of corpus. It conducted the text mining analysis techniques of frequency analysis, keyword analysis and the association rule analysis of keywords, based on the above-mentioned keywords of the classification system. As a result, the frequency of the keywords of 'general environment' and 'climate' accounted for 77 % of the total proportion and the keywords of 'public convergence service' and 'industrial convergence service' in the ICT field took up approximately 30 % of the total proportion. According to the time series analysis, the researches using ICT in the environmental field rapidly increased over the past 5 years (2011-2015) and the number of such researches more than doubled compared to the past (1996-2010). Based on the environmental field with generated association rules among the keywords, it was identified that the keyword 'general environment' was using 16 ICT-based technologies and 'climate' was using 14 ICT-based technologies.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.101-110
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• 2015

Synthetic Aperture Radar Interferometry (InSAR) is affected by various noise source such as atmospheric artifact, orbital error, processing noise etc.. Especially, one of the dominant noise source for long-wave SAR system, such as ALOS PALSAR (L-band SAR satellite) is the ionosphere effect because phase delays on radar pulse through the ionosphere are proportional to the radar wavelength. To avoid misinterpret of phase signal in the interferogram, it is necessary to detect and correct ionospheric errors. Recently, a MAI (Multipler Aperture SAR Interferometry) based ionospheric correction method has been proposed and considered one of the effective method to reduce phase errors by ionospheric effect. In this paper, we introduce the MAI-based method for ionospheric correction. Moreover we propose an efficient method that apply the method over non-coherent area using directional filter. Finally, we apply the proposed method to the ALOS PALSAR pairs, which include the west sea coast region in Korea. A polynomial fitting method, which is frequently adopted in InSAR processing, has been applied for the mitigation of phase distortion by the orbital error. However, the interferogram still has low frequency of Sin pattern along the azimuth direction. In contrast, after we applied the proposed method for ionospheric correction, the low frequency pattern is mitigated and the profile results has stable phase variation values within ${\pm}1rad$. Our results show that this method provides a promising way to correct orbital and ionospheric artifact and would be important technique to improve the accuracy and the availability for L-band or P-band systems.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.871-884
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• 2021

The purpose of this study was to determine ways to increase efficiency in constructing and verifying artificial intelligence learning data on land cover using aerial and satellite images, and in applying the data to AI learning algorithms. To this end, multi-resolution datasets of 0.51 m and 10 m each for 8 categories of land cover were constructed using high-resolution aerial images and satellite images obtained from Sentinel-2 satellites. Furthermore, fine data (a total of 17,000 pieces) and coarse data (a total of 33,000 pieces) were simultaneously constructed to achieve the following two goals: precise detection of land cover changes and the establishment of large-scale learning datasets. To secure the accuracy of the learning data, the verification was performed in three steps, which included data refining, annotation, and sampling. The learning data that wasfinally verified was applied to the semantic segmentation algorithms U-Net and DeeplabV3+, and the results were analyzed. Based on the analysis, the average accuracy for land cover based on aerial imagery was 77.8% for U-Net and 76.3% for Deeplab V3+, while for land cover based on satellite imagery it was 91.4% for U-Net and 85.8% for Deeplab V3+. The artificial intelligence learning datasets on land cover constructed using high-resolution aerial and satellite images in this study can be used as reference data to help classify land cover and identify relevant changes. Therefore, it is expected that this study's findings can be used in the future in various fields of artificial intelligence studying land cover in constructing an artificial intelligence learning dataset on land cover of the whole of Korea.