• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1935-1943
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• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.246-249
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• 2005

Satellite ground track display computer program is designed and implemented for the KOMPSAT-2 mission operations. Digitized world map and detailed Korean map is realized with zoom and pan capability. The program supports real-time ground trace and off-line satellite image planning on the world map. Satellite mission timeline is also displayed with the satellite ground track for the visualized mission operations. In this paper, the satellite ground track display is described in the aspect of the functional requirements, design, and implementation.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.821-826
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• 2002

The KOMPSAT-2 DCSU(the data compression & storage unit) performs the acquisition of image data from cameras, the compression with requested compression rate, the storage with specified file ID on the mission command and the distribution to the assigned DLS(Data Link System) channels per the mission and operation requirements. The worldwide observation using the MSC is able to be achieved by this DCSU's behavior. This paper presents the features of KOMPSAT-2 DCSU and provides proper ground operation concept after launch.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.4
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• pp.275-281
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• 2019

A synthetic aperture radar(SAR) satellite is more effective than an optical satellite for target analysis because an SAR satellite can provide two-dimensional electromagnetic scattering distribution of a target during all-weather and day-and-night operations. To conduct target analysis while considering the earth observation interval of an SAR satellite, observing a specific area as wide as possible would be advantageous. However, wider the observation area, worse is the resolution of the associated SAR satellite image. Although conventional methods for improving the resolution of radar images can be employed for addressing this issue, few studies have been conducted for improving the resolution of SAR satellite images and analyzing the performance. Hence, in this study, the applicability of conventional methods to SAR satellite images is investigated. SAR target detection was first applied to Korea Multipurpose Satellite-5(KOMPSAT-5) SAR images provided by Korea Aerospace Research Institute for extracting target responses. Extrapolation, RELAX, and MUSIC algorithms were subsequently applied to the target responses for improving the resolution, and the corresponding performance was thereby analyzed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.327-343
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• 2020

This study proposes a method to improve the geometric quality of KOMPSAT (Korea Multi-Purpose Satellite)-3/3A Level 1R imagery, particularly for efficient disaster damage analysis. The proposed method applies a novel grid-based SIFT (Scale Invariant Feature Transform) method to the Planetscope ortho-imagery, which solves the inherent limitations in acquiring appropriate optical satellite imagery over disaster areas, and the KOMPSAT-3/3A imagery to extract GCPs (Ground Control Points) required for the RPC (Rational Polynomial Coefficient) bias compensation. In order to validate its effectiveness, the proposed method was applied to the KOMPSAT-3 multispectral image of Gangnueng which includes the April 2019 wildfire, and the KOMPSAT-3A image of Daejeon, which was additionally selected in consideration of the diverse land cover types. The proposed method improved the geometric quality of KOMPSAT-3/3A images by reducing the positioning errors(RMSE: Root Mean Square Error) of the two images from 6.62 pixels to 1.25 pixels for KOMPSAT-3, and from 7.03 pixels to 1.66 pixels for KOMPSAT-3A. Through a visual comparison of the post-disaster KOMPSAT-3 ortho-image of Gangneung and the pre-disaster Planetscope ortho-image, the result showed appropriate geometric quality for wildfire damage analysis. This paper demonstrated the possibility of using Planetscope ortho-images as an alternative to obtain the GCPs for geometric calibration. Furthermore, the proposed method can be applied to various KOMPSAT-3/3A research studies where Planetscope ortho-images can be provided.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.777-786
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• 2018

GF (Guided Filtering) is a representative image processing technique to effectively remove noise while preserving edge information in the digital image. In this paper, we proposed a unsupervised change detection method for the KOMPSAT-3 satellite image using the GF and evaluated its performance. In order to utilize GF for the unsupervised change detection, cross-sharpened images were generated based on GF, and CVA (Change Vector Analysis) was applied to the generated cross-sharpened images to extract the changed area in the multitemporal satellite imagery. Experimental results using KOMPSAT-3 satellite images showed that the proposed method can be effectively used to detect changed regions compared with CVA results based on existing cross-sharpened images.

• Korean Journal of Remote Sensing
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• v.19 no.1
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• pp.43-52
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• 2003

EOC (Electro-Optical Camera) of KOMPSAT-1 (Korea Multi-Purpose SATellite) has been producing land imageries of the world since January 2000. After image data are acquired by EOC, they are transmitted from satellite to ground via X-band RF signal. Then, EOC image data are retrieved and pass through radiometric and geometric corrections to generate standard products of EOC images. After radiometric correction on EOC image data, Modulation Transfer Function (MTF) compensation is applicable on EOC images with user's request for better image quality. MTF compensation is concerned with filtering EOC images to minimize the effect of degradations. For Image Receiving and Processing System (IRPE) at KOMPSAT Ground Station (KGS), Wiener filter is used for MTF compensation of EOC images. If the Pointing Spread Function (PSF) of EOC system is known, signal-to-noise (SNR) power spectra ratio is the only variable which determines the shape of Wiener filter In this paper, MTF compensation in IRPE at KGS is briefly addressed, and MTF compensated EOC images are generated using Wiener filters with various SNR power spectra ratios. MTF compensated EOC images are compared with original EOC 1R images to observe correlations between them. As a result, the effect of SNR power spectra ratio on MTF compensated EOC images is shown.

• Proceedings of the KSRS Conference
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• v.2
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• pp.808-811
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• 2006

The KOMPSAT-2 was developed by KARI and it was successfully launched from Plesetsk, Russia on 28th July 2006. The Korean government decided the commercialization of the KOMPSAT-2 image data and direct reception services worldwide. SPOT Image, based in Toulouse (France) was selected by KARI through an international open bidding as a foreign company for the KOMPSAT-2 image promotion over the entire world except the territory of Republic of Korea including the North Korea, the United States of America, UAE, Saudi Arabia, Kuwait, Qatar, Oman, Yemen, Egypt, Iran, Iraq, Jordan, Lebanon, and Syria. KAI (Korea Aerospace Industry Ltd.) is an engaged Korean company for this area. KARI has responsibility to operate the satellite, data acquisition, archiving for the worldwide commercialization. For the processing and delivery of the KOMPSAT-2 image data to the users of KAI and SPOT Image, KAI has the binding contract with KARI. So KAI has the responsibility for the commercial ground station operation such as user support, data processing, and the data delivery. The KOMPSAT-2 ground station is hosted in KARI, so KARI has developed the concept of KOCUST (KOMPSAT-2 Commercial User Support Team) jointly with KAI to support the data processing and delivery as KOMPSAT-2 developer and satellite operator. The main purpose of the KOCUST is to support the operational activities to provide the data and service quality to satisfy customers. KOCUST will be organized by the members of KARI and KAI together. KARI members will mainly take the role of KOCUST coordination, data processing and user support in a public sector. KAI members are going to take user desk, data validation and delivery et cetera, which are related with users. This paper describes a summarized concepts of KOCUST like organization, dedicated tasks of each part and work flow of daily operation.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.6
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• pp.309-316
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• 2018

The high-resolution satellite images provided by Kompsat-3A, a multipurpose satellite, have various applications such as digital map generation, 3D image generation, and DEM generation. In order to utilize high-resolution satellite images, the user must create an orthoimage in order to use the image in a suitable manner. The position and the number of the ground reference points affect the accuracy of the orthoimage. In particular, the Kompsat-3A satellite image has a high resolution of about 0.5m, so the difficulty in selecting the ground control points and the accuracy of the selected point will have a great influence on the subsequent application process. Therefore, in this study, we analyzed the influence of the number of ground reference points on the accuracy of the terrestrial satellite images.