• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.75-86
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• 2010

As the performance of the spaceborne SAR has been dramatically enhanced and demonstrated through advanced missions such as TerraSAR and LRO(Lunar Reconnaissance Orbiter), the need for highly sophisticated and efficient SAR processor is also highlighted. In Korea, the activity of SAR researches has been mainly concerned with SAR image applications and the current SAR raw data studies are mostly limited to stripmap mode cases. The first Korean spaceborne SAR is scheduled to be operational from 2010 and expected to deliver vast amount of SAR raw data acquired from multiple operational scenarios including ScanSAR mode. Hence there will be an increasing demand to implement ground processing systems that enable to analyze the acquired ScanSAR data and generate corresponding images. In this paper, we have developed an efficient ScanSAR processor that can be directly applied to spaceborne ScanSAR mode data. The SPECAN(Spectrum Analysis) algorithm is employed for this purpose and its performance is verified through RADARSAT-1 ScanSAR raw data taken over Korean peninsular. An efficient quick-look processing is carried out to produce a wide-swath SAR image and compared with the conventional RDA processing case.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.859-865
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• 2018

A construction of infrastructures and base station on the moon could be undertaken by linking with the regions where construction materials and energy could be supplied on site. It is necessary to detect craters on the lunar surface and gather their topological information in advance, which forms permanent shaded regions (PSR) in which rich ice deposits might be available. In this study, an effective method for automatic detection of lunar craters on the moon surface is taken into consideration by employing a latest version of deep-learning algorithm. A training of a deep-learning algorithm is performed by involving the still images of 90000 taken from the LRO orbiter on operation by NASA and the label data involving position and size of partly craters shown in each image. the Faster RCNN algorithm, which is a latest version of deep-learning algorithms, is applied for a deep-learning training. The trained deep-learning code was used for automatic detection of craters which had not been trained. As results, it is shown that a lot of erroneous information for crater's positions and sizes labelled by NASA has been automatically revised and many other craters not labelled has been detected. Therefore, it could be possible to automatically produce regional maps of crater density and topological information on the moon which could be changed through time and should be highly valuable in engineering consideration for lunar construction.

• Journal of Space Technology and Applications
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• v.2 no.3
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• pp.195-205
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• 2022

Space exploration is an area where international cooperation takes place more actively than any other space activities such as Earth observation, communication and navigation. This is because a country cannot afford a huge budget to have full infrastructure for deep space exploration, such as a heavy launch vehicle, communication and energy infrastructure, and human habitats, and has learned that it is not sustainable. Korea expressed its willingness to join humanity's epic exploration journey by signing the Artemis Accords in 2021 and launching Danuri lunar orbiter in 2022. The beginning of space exploration means that Korea's space activities have expanded beyond the stage of focusing only on technology development to set norms necessary to accompany other countries and cooperate diplomatically to solve exposed problems. This paper analyzed European space policy and space exploration, which are most actively participating in the Artemis Program and exerting diplomatic power in the space field, from the perspective of science and technology diplomacy. The suggestions for Korea's space exploration strategy from the perspective of science and technology diplomacy were drawn by examining the international cooperation strategies in Europe's space activities ranging from space policy, space strategy, and space exploration program to project units.

• Journal of Space Technology and Applications
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• v.4 no.2
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• pp.121-136
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• 2024

Korea Pathfinder Lunar Orbiter (KPLO), launched in August 2022, is successfully carrying out its mission. Korea's lunar lander and rover programs are expected to proceed in the future. To successfully carry out the mission after the lunar lander has landed on the surface, the performance of the equipment to be mounted should be checked in a laboratory environment similar to the Moon. Scientists and engineers of several countries, including the United States and China, use lunar soil simulant which is developed to resemble lunar soil for simulating the surface of the lunar landing site. Several lunar probe landing sites are being discussed in Korea, and lunar soil simulants such as Korea Hanyang Lunar Simulant-1 (KOHLS-1), Korea Aerospace University Mechanical Lunar Simulants (KAUMLS), and Korea Lunar Simulant-1 (KLS-1), which are similar to the characteristics of lunar mare soil, have been developed. However, those simulants are not useful if the landing site is chosen as a highland area. In this study, we introduce the process of developing KIGAM-L1, a lunar highland soil simulant similar to the chemical composition of the Apollo 16 lunar soil sample and the particle size distribution of lunar soil sample 60500-1, in case the lunar lander lands at highland area.