• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.375-383
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• 2007

This paper presents the method to provide a fast service for user in image manipulation such as zooming and panning of huge size high resolution satellite image(e.g. Giga bytes per scene). The proposed technique is based on the hierarchical structure that has 3D-Tiling in horizontal and vertical direction to provide the image service more effectively than 2D-Tiling technique in the past does. The essence of the proposed technique is to create tiles of optimum level in real time on the basis of current displaying area, which change as user manipulates huge image. Consequently, this technique provides seamless service, and will be very powerful and useful for manipulation of images of huge size without data conversion.

• Proceedings of the KSRS Conference
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• v.1
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• pp.356-359
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• 2006

The high prices and lack of information for satellite images prevent researchers from studying remote sensing and most non-professional people can't have the simple and easy solutions for the manipulation of satellite images. 'Satellite Imagery Information Management Center'(SIMC) project which is promoted by ETRI (Electronics and Telecommunications Research Institute) from 2002 to 2005 in Korea have the purpose to provide the satellite images freely to the public domain and the solutions for the above mentioned problems. Our project have the following five systems; Data Acquisition System, Data Preservation System, Integrated Solution System, Technology Development System, Operation Plan System. Data Acquisition System collects the satellite images such as LANDSAT, IKONOS, etc. Data Preservation System consists of database which registers the diverse satellite images. Integrated Solution System gives the user of public domain for the web service which search, order and transfer the satellite images. Technology Development System has the many processing technologies for the satellite images. Finally, the Operation Plan system has the role to plan the future of our SIMC project. In this paper, we will give the result of SIMC Project for each five systems during the fast four years from 2002 to 2005.

• ETRI Journal
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• v.30 no.6
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• pp.774-782
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• 2008

An operational orbit determination (OD) and prediction system for the geostationary Communication, Ocean, and Meteorological Satellite (COMS) mission requires accurate satellite positioning knowledge to accomplish image navigation registration on the ground. Ranging and tracking data from a single ground station is used for COMS OD in normal operation. However, the orbital longitude of the COMS is so close to that of satellite tracking sites that geometric singularity affects observability. A method to solve the azimuth bias of a single station in singularity is to periodically apply an estimated azimuth bias using the ranging and tracking data of two stations. Velocity increments of a wheel off-loading maneuver which is performed twice a day are fixed by planned values without considering maneuver efficiency during OD. Using only single-station data with the correction of the azimuth bias, OD can achieve three-sigma position accuracy on the order of 1.5 km root-sum-square.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.69-73
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• 2017

In this paper, we developed a real-time image recognition algorithm based on machine learning and tested the performance of the algorithm. The real-time image recognition algorithm recognizes the input image in real-time based on the machine-learned image data. In order to test the performance of the real-time image recognition algorithm, we applied the real-time image recognition algorithm to the autonomous vehicle and showed the performance of the real-time image recognition algorithm through the application of the autonomous vehicle.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.147-150
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• 2008

This study refers to develop a semi-automatic extraction of agricultural land use and vegetation information using high resolution satellite images. Data of IKONOS satellite image (May 25 of 2001) and QuickBird satellite image (May 1 of 2006) which resembles with the spatial resolution and spectral characteristics of KOMPSAT3. The precise agricultural land use classification was tried using ISODATA unsupervised classification technique and the result was compared with on-screen digitizing land use accompanying with field investigation. For the extraction of vegetation information, three crops of paddy, com and red pepper were selected and the spectral characteristics were collected during each growing period using ground spectroradiometer. The vegetation indices viz. RVI, NDVI, ARVI, and SAVI for the crops were evaluated. The evaluation process is under development using the ERDAS IMAGINE Spatial Modeler Tool.

• 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.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1469-1478
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• 2018

Earthquakes and volcanic eruptions are disaster that causes billions of dollars in property damage and the loss of human life. Therefore, it is required to effectively monitor earthquakes and volcanoes. With the increase of satellite data, researches on earthquake and volcano using satellite imagery has been improved. Satellite images can be divided into three types i.e. optical, thermal, Synthetic Aperture Radar (SAR) and each image has different characteristics. In this article, we summarized its advantages and disadvantages of each type of satellite image. Moreover, we investigated the previous researches about earthquake and volcano using satellite images. Finally, we suggest application method to respond earthquake and volcano disaster using satellite images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.211-218
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• 2019

The Ministry of Land, Infrastructure and Transport is planning to launch CAS-500 (Compact Advanced Satellite 500) 1 and 2 in 2019 and 2020. Satellite image information collected through CAS-500 can be used in various fields such as global environmental monitoring, topographic map production, analysis for disaster prevention. In order to utilize in various fields like this, it is important to get the location accuracy of the satellite image. In order to establish the precise geometry of the satellite image, it is necessary to establish a precise sensor model using the GCP (Ground Control Point). In order to utilize various fields, step - by - step automation for orthoimage construction is required. To do this, a database of satellite image GCP chip should be structured systematically. Therefore, in this study, we will analyze various techniques for automatic GCP extraction for precise geometry of satellite images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.4
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• pp.359-365
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• 2004

Data integration techniques are becoming increasing1y important for conquering a limitation with a single data. Image fusion which improves the spatial and spectral resolution from a set of images with difffrent spatial and spectral resolutions, and image registration which matches two images so that corresponding coordinate points in the two images correspond to the same physical region of the scene being imaged have been researched. In this paper, we compared with six image fusion methods(Brovey, IHS, PCA, HPF, CN, and MWD) with panchromatic and multispectral images of IKONOS and developed the registration method for applying to SPOT-5 satellite image and RADARSAT SAR satellite image. As the result of tests on image fusion and image registration, we could find that MWD and HPF methods showed the good result in term of visual comparison analysis and statistical analysis. And we could extract patches which depict detailed topographic information from SPOT-5 and RADARSAT and obtain encouraging results in image registration.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.109-113
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• 2007

The COMS provides the LRIT/HRIT services to users. The COMS LRIT/HRIT broadcast service should satisfy the 15 minutes timeliness requirement. The requirement is important and critical enough to impact overall performance of the LHGS. HRIT image data is acquired from INRSM output receiving but LRIT image data is generated by sub-sampling HRIT image data in the LHGS. Specially, since LRIT is acquired from sub-sampled HRIT image data, LRIT processing spent more time. Besides, some of data loss for LRIT occurs since LRIT is compressed by lossy JPEG. Therefore, algorithm with the fastest processing speed and simplicity to be implemented should be selected to satisfy the requirement. Investigated sub-sampling algorithm for the LHGS were nearest neighbour algorithm, bilinear algorithm and bicubic algorithm. Nearest neighbour algorithm is selected for COMS LHGS considering the speed, simplicity and anti-aliasing corresponding to the guideline of user (KMA: Korea Meteorological Administration) to maintain the most cloud itself information in a view of meteorology. But the nearest neighbour algorithm is known as the worst performance. Therefore, it is studied in this paper that the selection of nearest neighbour algorithm for the LHGS is reasonable. First of all, characteristic of 3 sub-sampling algorithms is studied and compared. Then, several sub-sampling algorithm were applied to MTSAT-1R image data corresponding to COMS HRIT. Also, resized image was acquired from sub-sampled image with the identical sub-sampling algorithms applied to sub-sampling from HRIT to LRIT. And the difference between original image and resized image is compared. Besides, PSNR and MSE are calculated for each algorithm. This paper shows that it is appropriate to select nearest neighbour algorithm for COMS LHGS since sub-sampled image by nearest neighbour algorithm is little difference with that of other algorithms in quality performance from PSNR.