• 대한원격탐사학회지
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• 제23권5호
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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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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• 제30권6호
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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.

• 한국위성정보통신학회논문지
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• 제12권3호
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• pp.69-73
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• 2017

본 논문에서는 기계학습 기반의 실시간 이미지 인식 알고리즘을 개발하고 개발한 알고리즘의 성능을 테스트 하였다. 실시간 이미지 인식 알고리즘은 기계 학습된 이미지 데이터를 바탕으로 실시간으로 입력되는 이미지를 인식한다. 개발한 실시간 이미지 인식 알고리즘의 성능을 테스트하기 위해 자율주행 자동차 분야에 적용해보았고 이를 통해 개발한 실시간 이미지 인식 알고리즘의 성능을 확인해보았다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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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.

• 대한원격탐사학회지
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• 제38권1호
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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.

• 대한원격탐사학회지
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• 제34권6_4호
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• pp.1469-1478
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• 2018

지진과 화산은 막대한 인적 물적 피해를 발생시킬 수 있는 재해로서 광범위하게 영향을 미치기 때문에 이에 대한 효과적인 모니터링이 요구된다. 위성 자료가 많아지면서 효율적 지진 화산 모니터링을 위한 위성영상 기반의 연구가 많이 발전하고 있다. 위성영상의 종류는 크게 광학, 열적외선, 영상레이더로 구분할 수 있으며 각 위성영상 종류에 따라 서로 다른 특성을 가지고 있다. 여기서는 우선 각 종류별 장점과 단점을 정리하였다. 또한 국내 연구자들에 의해 수행된 위성 영상을 활용한 지진과 화산 관련 연구를 살펴보고 이를 토대로 지진과 화산 재해 대응을 위한 위성 영상의 종류별 활용 방안을 제시하고자 한다.

• 한국측량학회지
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• 제37권4호
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• pp.211-218
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• 2019

국토교통부에서는 2019년과 2020년에 차세대 중형위성 1호 및 2호 기를 발사하여, 지구환경 모니터링 및 접근불능지역에 대한 수치지도 제작에 활용하고자 하고 있다. 차세대 중형 위성을 통해 수집된 위성영상정보는 지구환경 모니터링, 지형도 제작, 재난재해 예방을 위한 분석 등 다양한 분야에 활용이 가능하다. 이와 같이 다양한 분야에 활용하기 위해서는 위성영상의 위치정확도 확보가 중요하며, 위성영상의 정밀기하수립을 위해 지표상의 정확한 지상기준점(GCP: Ground Control Point)을 사용하여 정밀 센서 모델을 수립하는 과정이 필요하다. 또한, 다양한 분야의 활용을 위해 정사영상 구축을 위한 단계별 자동화가 필요하며, 이를 위해 위성영상 GCP 칩의 DB (Data Base)가 체계적으로 구축되어야 한다. 따라서 본 연구에서는 위성영상의 정밀기하수립을 위하여 GCP를 자동 추출하는 다양한 기법들을 분석하여 최적의 방법을 도출하고자 한다.

• 한국측량학회지
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• 제22권4호
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• pp.359-365
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• 2004

많은 응용분야에서 단일 자료가 가진 한계를 극복하기 위해 다중 자료를 이용하여 통합 활용하는 기법이 요구되고 있다. 특히, 서로 다른 공간해상도와 분광해상도를 가진 영상들을 이용하여 영상의 공간해상도를 향상시키는 영상융합과 두 자료간의 상호 관계를 설정하는 영상등록에 대한 많은 연구가 진행되고 있다. 본 연구에서는 IKONOS 전정색 영상과 다중분광 위성영상에 대해 Brovey, IHS, PCA, HPF, CN, MWD 융합기법을 적용하여 원 영상의 분광정보를 가장 적게 왜곡하는 융합기법 에 대해 고찰하였다. 또한, SPOT-5 위성영상과 RADARSAT SAR 위성영상 간에 패치를 이용한 영상정합 기법을 적용하여 해석하였다. 본 연구를 통해 영상 융합에서 시각적 분석 및 통계적 분석 결과 HPF, MWD 융합기법이 가장 좋은 성과를 나타냈었으며, SPOT-5 위성영상과 RADARSAT SAR 위성영상으로부터 지형정보를 세밀하게 표현할 수 있는 패치를 추출함으로써 효과적인 영상등록이 가능하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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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.