• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.727-731
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• 2003

ASTER image has some advantages for classification such as 15 spectral bands and 15m ${\sim}$ 90m spatial resolution. However, in the classification using general remote sensing image, shadow areas are often classified into water area. It is very difficult to divide shadow and water. Because reflectance characteristics of water is similar to characteristics of shadow. Many land cover items are consisted in one pixel which is 15m spatial resolution. Nowadays, very high resolution satellite image (IKONOS, Quick Bird) and Digital Surface Model (DSM) by air borne laser scanner can also be used. In this study, mixed pixel analysis of ASTER image has carried out using IKONOS image and DSM. For mixed pixel analysis, high accurated geometric correction was required. Image matching method was applied for generating GCP datasets. IKONOS image was rectified by affine transform. After that, one pixel in ASTER image should be compared with corresponded 15×15 pixel in IKONOS image. Then, training dataset were generated for mixed pixel analysis using visual interpretation of IKONOS image. Finally, classification will be carried out based on Linear Mixture Model. Shadow extraction might be succeeded by the classification. The extracted shadow area was validated using shadow image which generated from 1m${\sim}$2m spatial resolution DSM. The result showed 17.2% error was occurred in mixed pixel. It might be limitation of ASTER image for shadow extraction because of 8bit quantization data.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.2 s.25
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• pp.61-66
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• 2003

The change detection using the satellite imagery and airphotos has been carried out in the application of terrain mapping, environment, forestry, facility detection, etc. The low-spatial resolution data such as Landsat, NOAA satellite images is generally used for automatic change detection, while on the other hand the high-spatial resolution data is used for change detection by image interpretation. The research to integrate automatic method with manual change detection through the high-spatial resolution satellite image is performed. but the problem such as shadow, building 'lean' due to perspective geometry and precision geocorrection was found. In this paper we performed change detection using the IKONOS satellite images, and present the concerning problem.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Journal of Astronomy and Space Sciences
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• v.8 no.1
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• pp.99-113
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• 1991

We developed a software system called IODS(ISSA Orbit Determination System), which can predict the orbit of arbitary artificial satellite using the numerical method. For evaluating the orbit prediction accuracy of IODS, the orbital data predicted for the meteorological satellite NOAA-11 and the stationary satellite INTELSAT-V are intercompared with those tracked at the Central Bureau of Meterology and the Kum-San Satellite Communication Station. And the Perturbations affecting the orbit of these artificial satellites are quantitatively analyzed. The orbital variation and the eclipse phenomina due to the shadow are analyzed for a hypothetical geostationary satellite called KORSAT-1 which is assumed to be located in longitude $110^{circ}E$.

• Proceedings of the KSRS Conference
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• v.1
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• pp.409-412
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• 2006

Today's commercial high resolution satellite imagery such as IKONOS and QuickBird, offers the potential to extract useful spatial information for geographical database construction and GIS applications. Recognizing this potential use of high resolution satellite imagery, KARI is performing a project for developing Korea multipurpose satellite 3(KOMPSAT-3). Therefore, it is necessary to develop techniques for various GIS applications of KOMPSAT-3, using similar high resolution satellite imagery. As fundamental studies for this purpose, we focused on the extraction of 3D spatial information and the update of existing GIS data from QuickBird imagery. This paper examines the scheme for rectification of high resolution image, and suggests the convenient semi-automatic algorithm for extraction of 3D building information from a single image. The algorithm is based on triangular vector structure that consists of a building bottom point, its corresponding roof point and a shadow end point. The proposed method could increase the number of measurable building, and enhance the digitizing accuracy and the computation efficiency.

• Journal of Space Technology and Applications
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• v.1 no.1
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• pp.49-63
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• 2021

The exploration of the solar system is carried out through various payloads, and accordingly, many research results are emerging. We tried to apply deep-learning as a method of studying the bodies of solar system. Unlike Earth observation satellite data, the data of solar system differ greatly from celestial bodies to probes and to payloads of each probe. Therefore, it may be difficult to apply it to various data with the deep-learning model, but we expect that it will be able to reduce human errors or compensate for missing parts. We have implemented a model that detects craters on the lunar surface. A model was created using the Lunar Reconnaissance Orbiter Camera (LROC) image and the provided shapefile as input values, and applied to the lunar surface image. Although the result was not satisfactory, it will be applied to the image of the permanently shadow regions of the Moon, which is finally acquired by ShadowCam through image pre-processing and model modification. In addition, by attempting to apply it to Ceres and Mercury, which have similar the lunar surface, it is intended to suggest that deep-learning is another method for the study of the solar system.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1911-1923
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• 2022

The availability of high-resolution satellite images provides precise information without direct observation of the research target. Korea Multi-Purpose Satellite (KOMPSAT), also known as the Arirang satellite, has been developed and utilized for earth observation. The machine learning model was continuously proven as a good classifier in classifying remotely sensed images. This study aimed to compare the performance of the support vector machine (SVM) model in classifying the land cover of the Delaware River port area on high and medium-resolution images. Three optical images, which are KOMPSAT-2, KOMPSAT-3A, and Sentinel-2B, were classified into six land cover classes, including water, road, vegetation, building, vacant, and shadow. The KOMPSAT images are provided by Korea Aerospace Research Institute (KARI), and the Sentinel-2B image was provided by the European Space Agency (ESA). The training samples were manually digitized for each land cover class and considered the reference image. The predicted images were compared to the actual data to obtain the accuracy assessment using a confusion matrix analysis. In addition, the time-consuming training and classifying were recorded to evaluate the model performance. The results showed that the KOMPSAT-3A image has the highest overall accuracy and followed by KOMPSAT-2 and Sentinel-2B results. On the contrary, the model took a long time to classify the higher-resolution image compared to the lower resolution. For that reason, we can conclude that the SVM model performed better in the higher resolution image with the consequence of the longer time-consuming training and classifying data. Thus, this finding might provide consideration for related researchers when selecting satellite imagery for effective and accurate image classification.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.507-519
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• 2017

The applications of NDVI (Normalized Difference Vegetation Index) as a vegetation index has been widely used to understand vegetation biomass and physiological activities. However, NDVI is not suitable way for monitoring vegetation stress because it is less sensitive to change in physiological state than biomass. PRI (Photochemical Reflectance Index) is well developed to present physiological activities of vegetation, particularly high-light-stress condition, and it has been adopted in several satellites to be launched in the future. Thus, the understanding of PRI performance and the development of analysis method will be necessary. This study aims to interpret the characteristics of light-stress-sensitive PRI in shadow areas and to evaluate the PRI calculated by other wavelengths (i.e., 488.9 nm, 553.6 nm, 646.9 nm, and 668.4 nm) instead of 570 nm that used in original PRI. Using airborne-based hyperspectral image, we found that PRI values were increased in shadow detection due to the reduction of high light induced physiological stress. However, the qualities of both PRI and NDVI data were dramatically decreased when the shadow index (SI) exceeded the threshold (SI<25). In addition, the PRI calculated using by 553.6 nm had best correlation with original PRI. This relationship was improved by multiple regression analysis including reflectances of RED and NIR. These results will be helpful to the understanding of physiological meaning on the application of PRI.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.274-274
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• 2002

Damage to forest trees, caused by wildfire, changes their spectral reflectance signature. This factor led to the initiation of a research project at the Remote Sensing & GIS Laboratory, Kookmin University, to determine if multispectral data acquired by IKONOS could provide fire scar and bum severity mapping. This paper will present detail mapping of burned areas in the eastern coast of Korea with IKONOS imagery. In addition, a single post-burn Landsat-7 ETM＋ data was used to compare with IKONOS, the study area. Burn severity map based on IKONOS image was found to be affected by strong topographic illumination effects in the mountain forest. But it has better the delineation of the bum-scarred area. In this study the NDVI was analyzed for geometric illumination conditions influenced by topography(slop, aspect and elevation) and shadow(solar elevation and azimuth angle).

• Journal of Astronomy and Space Sciences
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• v.37 no.1
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• pp.29-34
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• 2020

On 21 August 2017, during 16:49 UT and 20:02 UT period, a total solar eclipse started. The totality shadow occurred over the United States in time between ~17:15 UT and ~18:47 UT. When the solar radiation is blocked by the moon, observations of the ionospheric parameters will be important in the space weather community. Fortunately, during this eclipse, two Swarm satellites (A and C) flied at about 445 km through lunar penumbra at local noon of United States in the upper ionosphere. In this work, we investigate the effect of the solar eclipse on electron density, slant total electron content (STEC) and electron temperature using data from Swarm mission over United States. We use calibrated measurements of plasma density and electron temperature. Our results indicate that: (1) the electron density and STEC have a significant depletion associated with the eclipse; which could be due to dominance of dissociative recombination over photoionization caused by the reduction of ionizing extreme ultraviolet (EUV) radiation during the eclipse time (2) the electron temperature decreases, compared with a reference day, by up to ~150 K; which could be due to the decrease in photoelectron heating from reduced photoionization.