• The Bulletin of The Korean Astronomical Society
• /
• v.43 no.1
• /
• pp.68.3-68.3
• /
• 2018

The MATS (Mesosphere Airglow / Aerosol Tomography Spectroscopy) satellite is a Swedish scientific microsatellite which Kyung Hee University participates in developing. The limb telescope of the MATS satellite is designed with linear astigmatism-free off axis optical configuration which allows wide field of view ($5.67^{\circ}{\times}0.91^{\circ}$). Here we present the full-field optical performance test setup that consists of a point source, a collimator, the limb telescope and a CCD (Charged Coupled Device). The incidence angle of the collimator was carefully controlled by the rotary stage under the limb telescope. The imaging tests represent expected results without dominant aberrations.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.85-88
• /
• 2009

In this paper, we propose a DRF-based object detection method using the object adaptive patch in the satellite imagery. It is a Discriminative Random Fields (DRF) based work, so the detection is done by labeling to the possible patches in the image. For the feature information of each patch, we use the multi-scale and object adaptive patch and its texton histogram, instead of using the single scale and fixed grid patch. So, we can include contextual layout of texture information around the object. To make object adaptive patch, we use "superpixel lattice" scheme. As a result, each group of labeled patches represents the object or object's presence region. In the experiment, we compare the detection result with a fixed grid scheme and shows our result is more close to the object shape.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.441-443
• /
• 2003

The Bamboo stands, which had been planted for agricultural uses, have been spreading from fields to hills and mountains beyond farmers’ expectation over the last 50 years. Bamboo stands yield bamboo shoots to be served as a vegetable dish (‘takenoko in Japanese’) every spring, and supply materials for bamboo crafts in various ways. Now, as bamboo products have lost their social demand, bamboo stands has been gradually abandoned. This has resulted in the wild and unfavorable spread of bamboo stands in many regions over the western half of Japan. This paper illustrates the unfavorable spread of bamboo stands regionally in the southern Kyoto from 1985 to 1999 and the Yamashiro area from 1948 to 2000 through the observation of satellite images and aerial photographs.

• Korean Journal of Remote Sensing
• /
• v.23 no.3
• /
• pp.221-227
• /
• 2007

We present an analysis of urban development and growth with reconnaissance satellite photographs of Columbus metropolitan area acquired by the Corona program in 1965. A two-dimensional polynomial linear transformation was used to rectify the photos against United State Geological Survey (USGS) Large-scale Digital Line Graph (DLG) data georeferenced to Universal Transverse Mercator (UTM) coordinates. The boundaries of the Columbus metropolitan area were extracted from the rectified Corona image mosaic using a Bayesian approach to image segmentation. The inferred 1965 urban boundaries were compared with 1976 USGS Land Use and Land Cover (LULC) data and boundaries derived from 1988 and 1994 Landsat TM images. The urban area in and around Columbus approximately doubled from 1965 to 1994 (${\sim}110%$) along with population growth from 1960 to 1998 (${\sim}50%$). Most of the urban expansion results from development of residential units.

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.158-158
• /
• 2006

• Korean Journal of Remote Sensing
• /
• v.36 no.5_3
• /
• pp.1095-1107
• /
• 2020

Flood monitoring using satellite data has been constrained by obtaining satellite images for flood peak and accurately extracting flooded areas from satellite data. Deep learning is a promising method for satellite image classification, yet the potential of deep learning-based flooded area extraction using SAR data remained uncertain, which has advantages in obtaining data, comparing to optical satellite data. This research explores the performance of SegNet and U-Net on image segmentation by extracting flooded areas in the Khorat basin, Mekong river basin, and Cagayan river basin in Thailand, Laos, and the Philippines from Sentinel-1 A/B satellite data. Results show that Global Accuracy, Mean IoU, and Mean BF Score of SegNet are 0.9847, 0.6016, and 0.6467 respectively, whereas those of U-Net are 0.9937, 0.7022, 0.7125. Visual interpretation shows that the classification accuracy of U-Net is higher than SegNet, but overall processing time of SegNet is around three times faster than that of U-Net. It is anticipated that the results of this research could be used when developing deep learning-based flood monitoring models and presenting fully automated flooded area extraction models.

• Korean Journal of Remote Sensing
• /
• v.22 no.6
• /
• pp.621-626
• /
• 2006

Today's commercial high resolution satellite imagery such as IKONOS and QuickBird, offers the potential to extract useful spatial information for geographical database construction and GIS applications. Extraction of 3D building information from high resolution satellite imagery is one of the most active research topics. There have been many previous works to extract 3D information based on stereo analysis, including sensor modelling. Practically, it is not easy to obtain stereo high resolution satellite images. On single image performance, most studies applied the roof-bottom points or shadow length extracted manually to sensor models with DEM. It is not suitable to apply these algorithms for dense buildings. We aim to extract 3D building information from a single satellite image in a simple and practical way. To measure as many buildings as possible, in this paper, we suggested a new way to extract building height by triangular vector structure that consists of a building bottom point, its corresponding roof point and a shadow end point. The proposed method could increase the number of measurable building, and decrease the digitizing error and the computation efficiency.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.8
• /
• pp.959-965
• /
• 2019

In this paper, we design a low noise amplifier to support ka-band satellite communication systems using 65-nm RFCMOS process. The proposed low noise amplifier is designed with high-gain mode and low-gain mode, and is designed to control the gain according to the magnitude of the input signal. In order to reduce the power consumption, the supply voltage of the entire circuit is limited to 1 V or less. We proposed the gain control circuit that consists of the inverter structure. The 3D EM simulator is used to reduce the size of the circuit. The size of the designed amplifier including pad is $0.33mm^2$. The fabricated amplifier has a -7 dB gain control range in 3 dB bandwidth and the reflection coefficient is less than -6 dB in high gain mode and less than -15 dB in low gain mode.

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.278-281
• /
• 2006

Korea Meteorological Administration(KMA) has issued the tropical storm(typhoon) warning or advisories when it was developed to tropical storm from tropical depression and a typhoon is expected to influence the Korean peninsula and adjacent seas. Typhoon information includes current typhoon position and intensity. KMA has used the Dvorak Technique to analyze the center of typhoon and it's intensity by using available geostationary satellites' images such as GMS, GOES-9 and MTSAT-1R since 2001. The Dvorak technique is so subjective that the analysis results could be variable according to analysts. To reduce the subjective errors, QuikSCAT seawind data have been used with various analysis data including sea surface temperature from geostationary meteorological satellites, polar orbit satellites, and other observation data. On the other hand, there is an advantage of using the Subjective Dvorak Technique(SDT). SDT can get information about intensity and center of typhoon by using only infrared images of geostationary meteorology satellites. However, there has been a limitation to use the SDT on operational purpose because of lack of observation and information from polar orbit satellites such as SSM/I. Therefore, KMA has established Advanced Objective Dvorak Technique(AODT) system developed by UW/CIMSS(University of Wisconsin-Madison/Cooperative Institude for Meteorological Satellite Studies) to improve current typhoon analysis technique, and the performance has been tested since 2005. We have developed statistical relationships to correct AODT CI numbers according to the SDT CI numbers that have been presumed as truths of typhoons occurred in northwestern pacific ocean by using linear, nonlinear regressions, and neural network principal component analysis. In conclusion, the neural network nonlinear principal component analysis has fitted best to the SDT, and shown Root Mean Square Error(RMSE) 0.42 and coefficient of determination($R^2$) 0.91 by using MTSAT-1R satellite images of 2005. KMA has operated typhoon intensity analysis using SDT and AODT since 2006 and keep trying to correct CI numbers.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.141-144
• /
• 2006

The deviations in the injection orbital parameters, resulting from launcher dispersions, need to be estimated and used for autonomous satellite operations. For the proposed small satellite mission of the university there will be two GPS receivers onboard the satellite to provide the instantaneous orbital state to the onboard data handling system. In order to meet the power requirements, the satellite will be sun-tracking whenever there is no imaging operation. For imaging activities, the satellite will be maneuvered to nadir-pointing mode. Due to such different modes of orientation the geometry for the GPS receivers will not be favorable at all times and there will be instances of poor geometry resulting in no output from the GPS receivers. Onboard the satellite, the orbital information should be continuously available for autonomous switching on/off of various subsystems. The paper presents the strategies to make use of small arcs of data from GPS receivers to compute the mean orbital parameters and use the updated orbital parameters to calculate the position and velocity whenever the same is not available from GPS receiver. Thus the navigation message from the GPS receiver, namely the position vector in Earth-Centered-Earth-Fixed (ECEF) frame, is used as measurements. As for estimation, two techniques - (1) batch least squares method, and (2) Kalman Filter method are used for orbit estimation (in real time). The performance of the onboard orbit estimation has been assessed based on hardware based multi-channel GPS Signal simulator. The results indicate good converge even with short arcs of data as the GPS navigation data are generally very accurate and the data rate is also fast (typically 1Hz).