• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.378-380
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• 2003

One of the most common forms of land use change is urbanization. Fortunately, the temporal revisit capacity of remote sensing satellites and their multispectral imaging capability make it possible to monitor this process. Using two Landsat images taken in 1972 and 1989, and one SPOT image taken in 2000, urban growth in Metro Manila is monitored. The extent of urbanization in Metro Manila increased from about 39 percent in 1972 to about 74 percent in 2000, although a slowing of growth was observed in the last decade due to decreasing areas for development. Most cities and municipalities in Metro Manila exhibited urban growth rates higher than the metropolitan average. The drivers and environmental consequences of urban growth were determined as well as the relationship of the extent of urbanization with some socio-economic and environmental variables.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.5
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• pp.493-504
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• 2010

This study proposes a classification method based on an automated training extraction procedure that may be used with very high resolution (VHR) images of non-accessible areas. The proposed method overcomes the problem of scale difference between VHR images and geographic information system (GIS) data through filtering and use of a Landsat image. In order to automate maximum likelihood classification (MLC), GIS data were used as an input to the MLC of a Landsat image, and a binary edge and a normalized difference vegetation index (NDVI) were used to increase the purity of the training samples. We identified the thresholds of an NDVI and binary edge appropriate to obtain pure samples of each class. The proposed method was then applied to QuickBird and SPOT-5 images. In order to validate the method, visual interpretation and quantitative assessment of the results were compared with products of a manual method. The results showed that the proposed method could classify VHR images and efficiently update GIS data.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.197-201
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• 2002

The high prices of satellite images prevent researchers from studying remote sensing and most non-professional people doesn't have the simple and easy solutions for the manipulation of satellite images. "Integrated Management of Satellite Imagery Information" project which will be promoted by ETRI (Electronics and Telecommunications Research Institute) will provide the solutions for the above mentioned problems. We will introduce the archiving center in this study. This includes the data construction, storage, management and distribution. We first review the background for this archiving center and introduce the interior and foreign institutes which archive and distribute satellite images. We review our H/W system and S/W system briefly. Finally, the further service of our project will be suggested. Since we will distribute the satellite images (Landsat, SPOT, JERS, Corona, Kompast-1) and will receive Landsat7 ETM+ in 2003 you, this will help the professional work dealing with the satellite image and attract the non-professional people for simple and easy manipulation solutions of satellite image.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.245-250
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• 2004

The classification of the satellite images is basic part in Remote sensing. In classification of the satellite images, class separability feature is very effective accuracy of the images classified. For improving classification accuracy, It is necessary to study classification methode than analysis of class separability feature deciding classification probability. In this study, IKONOS, SPOT 5, Landsat TM, were resampled to sizes 1m grid. Above images were calculated the class separability prior to the step for classification of pixels. The results of the study were valued necessary process in geometric information building. This study help to improve accuracy of classification as feature of class separability in the class through optimizing previous classification steps.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.648-650
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• 2003

Efficient multi-resolution image fusion aims to take advantage of the high spectral resolution of Landsat TM images and high spatial resolution of SPOT panchromatic images simultaneously. This paper presents a multi-resolution data fusion scheme, based on multirate image representation. Motivated by analytical results obtained from high-resolution multispectral image data analysis: the energy packing the spectral features are distributed in the lower frequency bands, and the spatial features, edges, are distributed in the higher frequency bands. This allows to spatially enhancing the multispectral images, by adding the high-resolution spatial features to them, by a multirate filtering procedure. The proposed method is compared with some conventional methods. Results show it preserves more spectral features with less spatial distortion.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.5
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• pp.67-77
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• 2007

The images of MODerate resolution Imaging Spectroradiometer (MODIS) that provide wider swath and shorter revisit frequency than Land Satellite (Landsat) and Satellite Pour I' Observation de la Terre (SPOT) has been used fer land cover classification with better spatial resolution than National Oceanic and Atmosphere Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR)'s images. Due to the advantages of MODIS, several researches have conducted, however the results for the land cover classification using MODIS images have less accuracy of classification in small areas because of low spatial resolution. In this study, uncertainty of paddy fields classification using MODIS images was conducted in the region of Gyeonggi-do and the relation between this uncertainty of estimating paddy fields and topographical factors was also explained. The accuracy of classified paddy fields was compared with the land cover map of Environmental Geographic Information System (EGIS) in 2001 classified using Landsat images. Uncertainty of paddy fields classification was analyzed about the elevation and slope from the 30m resolution Digital Elevation Model (DEM) provided in EGIS. As a result of paddy classification, user's accuracy was about 41.5% and producer's accuracy was 57.6%. About 59% extracted paddy fields represented over 50 uncertainty in one hundred scale and about 18% extracted paddy fields showed 100 uncertainty. It is considered that several land covers mixed in a MODIS pixel influenced on extracted results and most classified paddy fields were distributed through elevation I, II and slope A region.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.96-99
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• 2003

Land cover map is a typical GIS database which shows the Earth's physical surface differentiated by standardized homogeneous land cover types. Satellite images acquired by Landsat TM were primarily used to produce a land cover map of 7 land cover classes; however, it now becomes to produce a more accurate land cover classification dataset of 23 classes thanks to higher resolution satellite images, such as SPOT-5 and IKONOS. The use of the newly produced high resolution land cover map of 23 classes for such activities to estimate non-point sources of pollution like water pollution modeling and atmospheric dispersion modeling is expected to result a higher level of accuracy and validity in various environmental monitoring results. The estimation of pollution from non-point sources using GIS-based modeling with land cover dataset shows fairly accurate and consistent results.

• Journal of the Korean Geographical Society
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• v.41 no.3 s.114
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• pp.331-345
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• 2006

This study deals with geomorphological process of the sand dune landform including the distribution and surface environments, characteristics of sediments, origins and moving processes in lower reach and mouth delta of Duman River, Northeast Korea and China. The methodology of the study includes image analysis of Landsat TM(1992.10) and ETM(2000.9) and Spot(2005.4) for analysis of land cover, 2 times field survey for recognition of landform and acquisition of sediments raw data materials, and grain analysis and exoscopy about raw data materials. The geomorphic elements from satellite image analysis are composed of the delta, sand spit, active and stable dune, sand bar and riparian vegetated zone. Results of the grain analysis indicate the sediments originated from marine coastal zone than riverine one. This means that present sand dune not so much reflect present climatic and geomorphic environments. Result of the exoscopy analysis show that ratio of quartz, which is comparatively resistant to environment, is highest as $65{\sim}83%$ out of sediments. But the surface of the $30{\sim}40%$ of mineral grains was coated by yellow-colored stained materials, due to chemical weathering. Some grains show rough skin, looking as acicular, network structure and etching pits, affected by physical and chemical weathering.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.195-208
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• 1999

In the classification of satellite images, the representative of training of classes is very important factor that affects the classification accuracy. Hence, in order to improve the classification accuracy, it is required to optimize pre-classification stage which determines classification parameters rather than to develop classifiers alone. In this study, the normality of training are calculated at the preclassification stage using SPOT XS and LANDSAT TM. A correlation coefficient of multivariate Q-Q plot with 5% significance level and a variance of initial training are considered as an object function of genetic algorithm in the training normalization process. As a result of normalization of training using the genetic algorithm, it was proved that, for the study area, the mean and variance of each class shifted to the population, and the result showed the possibility of prediction of the distribution of each class.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.306-309
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• 2004

The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the Geographic Information System (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat TM (Thermatic Mapper) satellite images; and the vegetation index value from SPOT HRV (High Resolution Visible) satellite images. Landslide hazardous areas were analysed and mapped using the landslide-occurrence factors employing the probability-frequency ratio method. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, land 'cover had relatively positive effects, and lithology had relatively negative effects on the landslide susceptibility maps in the study area. In addition, the landslide susceptibility maps using the all factors showed the relatively good results.