• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.361-369
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• 1998

Recently, the improvement of on-board satellite sensors covering hyperspectral image sensors, high spatial resolution sensors provide data on earth in diverse aspect. The application field relating remotely sensed data also varies depending on what type of job one wants. The various resolution of sensors from low to extremely high is also available on the market with a user defined specific location. The expense to purchase remote sensed data is going down compare to the cost it need past few years ago in terms of research or private use. Now, the satellite remote sensed data is used on the field of forecasting, forestry, agriculture, urban reconstruction, geology, or other research field in order to extract meaningful information by applying special techniques of image processing. There are many image processing packages available worldwide and one common aspect is that they are expensive. There need to be a advanced satellite data processing package for people who can not afford commercial packages to apply special remote sensing techniques on their data and produce valued-added product. The study was carried out with the purpose of developing a special satellite data processing package which covers almost every satellite produced data with normal image processing functions and also special functions needed on specific research field with friendly graphical user interface (GUI). And for the people with any background of remote sensing with windows platform.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.792-797
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• 2002

In the following paper two algorithms, suitable for the analysis of panchromatic data as provided by KOMPSAT-1 will be presented. One is a texture analysis which will be used to create a settlement mask based on the variations of gray values. The other is a fusion algorithm which allows the combination of high resolution panchromatic data with medium resolution multispectral data. The procedure developed for this purpose uses the spatial information present in the high resolution image to spatially enhance the low resolution image, while keeping the distortion of the multispectral information to a minimum. This makes it possible to use the fusion results for standard multispecatral classification routines. The procedures presented here can be automated to large extent, making them suitable for a standard processing routine of satellite data.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.231-236
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• 1999

Accurate ocean surface fluxes with high resolution are critical for understanding a mechanism of global climate. However, it is difficult to derive those fluxes by using ocean observation data because the number of ocean observation data is extremely small and the distribution is inhomogeneous. On the other hand. satellite data are characterized by the high density, the high resolution and the homogeneity. Therefore, it can be considered that we obtain accurate ocean surface by using satellite data. Recently we constructed ocean surface data sets mainly using satellite data. The data set is named by Japanese Ocean Flux data sets with Use of Remote sensing Observations (J-OFURO). Here, we introduce J-OFURO. The data set includes shortwave radiation, longwave radiation, latent heat flux, sensible heat flux, and momentum flux etc. Moreover, sea surface dynamic topography data are included in the data set. Radiation data sets covers western Pacific and eastern Indian Ocean because we use a Japanese geostationally satellite (GMS) to estimate radiation fluxes. On the other hand, turbulent heat fluxes are globally estimated. The constructed data sets are used and shows the effectiveness for many scientific studies.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.683-691
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• 2012

High resolution satellite images are now widely used for a variety of mapping applications including photogrammetry, GIS data acquisition and visualization. As the spectral and spatial data size of satellite images increases, a greater processing power is needed to process the images. The solution of these problems is parallel systems. Parallel processing techniques have been developed for improving the performance of image processing along with the development of the computational power. However, conventional CPU-based parallel computing is often not good enough for the demand for computational speed to process the images. The GPU is a good candidate to achieve this goal. Recently GPUs are used in the field of highly complex processing including many loop operations such as mathematical transforms, ray tracing. In this study we proposed a technique for parallel processing of high resolution satellite images using GPU. We implemented a spectral radiometric processing algorithm on Landsat-7 ETM+ imagery using CUDA, a parallel computing architecture developed by NVIDIA for GPU. Also performance of the algorithm on GPU and CPU is compared.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.301-310
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• 2003

In this paper, we design and Implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the m()st effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.379-383
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• 2006

Satellite imagery may contain large regions covered with atmospheric aerosol. A highresolution satellite imagery affected by non-homogenous aerosol cover should be processed for land cover study and perform the radiometric calibration that will allow its future application for Korea Multi-Purpose Satellite (KOMPSAT) data. In this study, aerosol signal was separated from high resolution satellite data based on the reflectance separation method. Since aerosol removal has a good sensitivity over bright surface such as man-made targets, aerosol optical thickness (AOT) retrieval algorithm could be used. AOT retrieval using Look-up table (LUT) approach for utilizing the transformed image to radiometrically compensate visible band imagery is processed and tested in the correction of satellite scenery. Moderate Resolution Imaging Spectroradiometer (MODIS), EO-l/HYPERION data have been used for aerosol correction and AOT retrieval with different spatial resolution. Results show that an application of the aerosol detection for HYPERION data yields successive aerosol separation from imagery and AOT maps are consistent with MODIS AOT map.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.2
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• pp.155-164
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• 2002

In this study, in order to discuss the geometric correction methods of high-resolution IKONOS satellite image, the existing polynomial model and RFM which is able to rectify satellite image without auxiliary data are applied to IKONOS satellite image data. Then the accuracy of ground point versus number of GCPs and each order of RFM are assessed. A numerical instability is removed by application of Tikhonov regularization method. As the results of this study, the root mean square errors of RFM is decreased more than 2 pixels in comparison with the two dimensional polynomial model.

• 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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.856-858
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• 2003

Biotop map can be utilized in the urban area for nature conservation and impact assessment for the proposed activities. High resolution satellite data such as IKONOS and KOMPSAT1-EOS were used to classify land use activities in biotop mapping. After land use classification, field -check was done to survey the wildlife and vegetation. These maps were combined and the boundaries were delineated to produce the biotop map. Within the boundary the characteristics of each polygon were identified, and named. This study was carried out at Daedok Science Town in Taejeon Metropolitan Area. The purpose of this study is to produce the biotop map using high resolution remote sensing data together with other ground data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.593-598
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• 2006

High-resolution satellite imagery has many benefits, compared to aerial photo in the wide area as well as multi-spectral character. So, it can be used well for constructing GIS data when making digital map. This study analysed the possibilities that road information derived automatically from IKONOS can be used for making ITS system or updating digital map of the urban areas where change frequently and producing satellite image map. In this study, Sobel was applied for road edge dectection after low pass filtering. As the results, it's possible for low pass filtering and high pass filtering to be used as the basic data for ITS construction when extracting edge roads and constructs according to the characteristic of high-resolution satellite imagery.