• Journal of Wetlands Research
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• v.7 no.2
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• pp.121-132
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• 2005

To classify the surface sedimentary facies using IKONOS image collected over Hwangdo tidal flat in Cheonsu Bay, the optical reflectance was compared for characterizing various sedimentary environments such as grain size, tidal channel pattern and area ratio of surface remnant water. The intertidal DEM (Digital Elevation Model) was generated by echo-sounder for analyzing the relationship between IKONOS image and sedimentary environments including topography. The boundary of the optical reflectance between mud-mixed facies and sand facies was distinct, and discrimination of the associated sandbar feature was also possible. The mud-mixed facies coupled with intricate tidal channels is confined to the relatively hi호 topography of Hwangdo tidal flat. The boundary between mud and mixed flat was indistinct in IKONOS optical reflectance but it would have a difference in the area ratio of surface remnant water. The dark area in the image represented the well developed sand facies having a lot of surface remnant water due to the relatively low surface topography. The overall accuracy of characterizing the surface sediment facies by maximum likelihood classification method was 86.2 %. These results demonstrate that high spatial resolution satellite imagery such as IKONOS coupled with knowledge of grain size, surface remnant water and tidal channel network can be effectively used to characterize the surface sedimentary facies (mud, mixed and sand) network of the tidal flat environments.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1271-1275
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• 2004

The purpose of this study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including Landsat +ETM, KOMPSAT-1 EOC, ASTER VNIR, and IKONOS panchromatic and multi-spectral images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by Ministry of Construction & Transportation based on NGIS (National Geographic Information System) and Ministry of Environment based on satellite remote sensing data. As a result, high-resolution agricultural land cover map from IKONOS imageries was made out. The results by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.261-265
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• 2004

The purpose of this study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including Landsat＋ETM, KOMPSAT-1 EOC, ASTER VNIR, and IKONOS panchromatic and multi-spectral images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by Ministry of Construction & Transportation based on NGIS (National Geographic Information System) and Ministry of Environment based on satellite remote sensing data. As a result, high-resolution agricultural land cover map from IKONOS imageries was made out. The results by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.57-59
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• 2003

Digital surface model generation from IKONOS stereo imagery is a new challenge in photogrammetric community, especially when the satellite company does not provide the raw data as well as their ancillary ephemeris data. In this paper we utilized an estimated relief displacement azimuth and the nominal collection elevation data included in the metadata file to correct the relief displacement of GCPs, together with a linear transformation for geometric modeling of IKONOS imagery. Space intersection is performed by the trigonometric intersection assuming a parallel projection of IKONOS imagery due to its small FOV and frame size. In the experiment, less than 2-meters of RMSE in orbit modeling is achieved denoting the potential positioning accuracy of the IKONOS stereo imagery.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1209-1211
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• 2003

The wavelet and semivariogram analysis method are used to identify the city landscape and farmland landscape pattern on the 1m resolution IKONOS images. The results prove that wavelet method is a potential way for landscape pattern analysis. Compared to semivariogram analysis, Wavelet analysis can not only detect the overall spatial pattern, but also find multi-scale and direction structures. In this experiment, the wavelet analysis results indicate: (1) the city landscape image is mainly composed of three level structures whose spatial pattern characters appear at 2m, 16m, 128m and 256m accordingly; (2) the farmland landscape is mainly two scale spatial patterns appearing at the 2m, 128m and 256m. IKONOS Remote sensing, with the high spatial and spectral information, is a powerful tool that can use in many ecological systems research and sustainable management.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.4
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• pp.103-109
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• 2007

Ortho images and Digital Elevation Model (DEM) have been applied in various fields. It is necessary to acquire Ground Control Points (GCPs) for processing high resolution satellite images. However surveying GCPs require many time and expense. This study was performed to investigate whether GCPs automatically extracted from ortho images and DTED Level 2 can be applied to sensor modeling for high resolution satellite images. We analyzed the performance of the sensor model established by GCPs extracted automatically. We acquired GCPs by matching satellite image against ortho images. We included the height acquired from DTED Level 2 data in these GCPs. The spatial resolution of the DTED Level 2 data is about 30m. Absolution accuracy of this data is below 18m above MSL. The spatial resolution of ortho image is 1m. We established sensor model from IKONOS images using GCPs extracted automatically and generated DEMs from the images. The accuracy of sensor modeling is about $4{\sim}5$ pixel. We also established sensor models using GCPs acquired based on GPS surveying and generated DEMs. Two DEMs were similar. The RMSE of height from the DEM by automatic GCPs and DTED Level 2 is about 9 m. So we think that GCPs by DTED Level 2 and ortho image can use for IKONOS sensor modeling.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.3
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• pp.183-190
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• 2007

To make use of surveying data obtained from different sensors and different techniques, it is a pre-requite step that register them in a common coordinate system. For this purpose, we developed methodologies to register IKONOS-2 Satellite Imagery using LiDAR(Light Detection And Ranging) data. To achieve this, conjugate features from these data should be extracted in advance. In this study, linear features are chosen as conjugate features. Then, to register them, observation equations are established from similarity measurements of the extracted features and the results was evaluated statistically. The results clearly demonstrate that the proposed algorithms are appropriate to register these data.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.344-346
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• 2004

IKONOS 1m satellite imagery is particularly well suited for 3-D feature extraction and 1 :5,000 scale topographic mapping. Because the image line and sample calculated by given RPCs have the error of more than 11m, in order to be able to perform feature extraction and topographic mapping, rational polynomial coefficients(RPCs) camera model that are derived from the very complex IKONOS sensor model to describe the object-image geometry must be refined by several Ground Control Points(GCPs). This paper presents a quantitative evaluation of the geometric accuracy that can be achieved with IKONOS imagery by refining the offset and scaling factors of RPCs using several GCPs. If only two GCPs are available, the offsets and scale factors of image line and sample are updated. If we have more than three GCPs, four parameters of the offsets and scale factors of image line and sample are refined first, and then six parameters of the offsets and scale factors of latitude, longitude and height are updated. The stereo images acquired by IKONOS satellite are tested using six ground points. First, the RPCs model was refined using 2 GCPs and 4 check points acquired by GPS. The results from IKONOS stereo images are reported and these show that the RMSE of check point acquired from left images and right are 1.021m and 1.447m. And then we update the RPCs model using 4 GCPs and 2 check points. The RMSE of geometric accuracy is 0.621 m in left image and 0.816m in right image.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.207-210
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• 2007

본 연구에서는 고해상도의 IKONOS panchromatic 영상과 multispectral 영상을 IHS와 PCA 방법으로 중합하고 그 결과를 비교하였다. 평가에 있어서는 중합된 영상들과 원영상간의 필셀 값에 대한 평균제곱근오차를 구하고 그 결과를 분석하였다. 분석 결과, multispectral band 1, 3, 4를 사용하는 IHS 방법, multispectral band 1, 2, 4를 사용하는 IHS 방법 및 multispectral band 1, 3, 4를 사용하는 PCA 방법이 원영상의 특성을 잘 보존하는 것으로 평가되었다.

• 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.