• Journal of Communications and Networks
• /
• v.8 no.4
• /
• pp.451-460
• /
• 2006

Since low earth orbit (LEO) satellite constellations have important advantages over geosynchronous earth orbit (GEO) systems such as low propagation delay, low power requirements, and more efficient spectrum allocation due to frequency reuse between satellites and spotbeams, they are considered to be used to complement the existing terrestrial fixed and wireless networks in the evolving global mobile network. However, one of the major problems with LEO satellites is their higher speed relative to the terrestrial mobile terminals, which move at lower speeds but at more random directions. Therefore, handover management in LEO satellite networks becomes a very challenging task for supporting global mobile communication. Efficient and accurate methods are needed for LEO satellite handovers between the moving footprints. In this paper, we propose a new seamless handover management scheme for LEO satellites (SeaHO-LEO), which utilizes the handover management schemes aiming at decreasing latency, data loss, and handover blocking probability. We also present another interesting handover management model called satellite mobility pattern based handover management in LEO satellites (PatHO-LEO) which takes mobility pattern of both satellites and mobile terminals into account to minimize the handover messaging traffic. This is achieved by the newly introduced billboard manager which is used for location updates of mobile users and satellites. The billboard manager makes the proposed handover model much more flexible and easier than the current solutions, since it is a central server and supports the management of the whole system. To show the performance of the proposed algorithms, we run an extensive set of simulations both for the proposed algorithms and well known handover management methods as a baseline model. The simulation results show that the proposed algorithms are very promising for seamless handover in LEO satellites.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1653-1661
• /
• 2022

In this study, the classification of cold water and normal water based on Geo-Kompsat 2A images was performed. Daily mean surface temperature products provided by the National Meteorological Satellite Center (NMSC) were used, and convolution neural network (CNN) deep learning technique was applied as a classification algorithm. From 2019 to 2022, the cold water occurrence data provided by the National Institute of Fisheries Science (NIFS) were used as the cold water class. As a result of learning, the probability of detection was 82.5% and the false alarm ratio was 54.4%. Through misclassification analysis, it was confirmed that cloud area should be considered and accurate learning data should be considered in the future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.4
• /
• pp.429-438
• /
• 2011

LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.775-779
• /
• 2002

Currently, high-resolution satellite imagery such as KOMPSAT and IKONOS has been tentatively utilized to various types of urban engineering problems such as transportation planning, site planning, and utility management. This approach aims at software development and followed applications of remotely sensed imagery to transportation geographic analysis. At first, GDPA (Gradient Direction Profile Algorithm) and main modules in it are overviewed, and newly implemented results under MS visual programming environment are presented with main user interface, input imagery processing, and internal processing steps. Using this software, road network are automatically generated. Furthermore, this road network is used to transportation geographic analysis such as gamma index and road pattern estimation. While, this result, being produced to do-facto format of ESRI-shapefile, is used to several types of road layers to urban/transportation planning problems. In this study, road network using KOMPSAT EOC imagery and IKONOS imagery are directly compared to multiple road layers with NGI digital map with geo-coordinates, as ground truth; furthermore, accuracy evaluation is also carried out through method of computation of commission and omission error at some target area. Conclusively, the results processed in this study is thought to be one of useful cases for further researches and local government application regarding transportation geographic analysis using remotely sensed data sets.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_1
• /
• pp.647-660
• /
• 2022

To prepare an atmospheric correction model suitable for CAS500-4 (Compact Advanced Satellite 500-4), this letter examined an atmospheric correction experiment using Sentinel-2 images having similar spectral characteristics to CAS500-4. Studies to compare the atmospheric correction results depending on different Aerosol Optical Depth (AOD) data are rarely found. We conducted a comparison of Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Sen2Cor, and Second Simulation of the Satellite Signal in the Solar Spectrum - Vector (6SV) version 1.1 and 2.1, using Geo-Kompsat 2A (GK2A) Advanced Meteorological Imager (AMI) and Aerosol Robotic Network (AERONET) AOD data. In this experiment, 6SV2.1 seemed more stable than others when considering the correlation matrices and the output images for each band and Normalized Difference Vegetation Index (NDVI).

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1767-1772
• /
• 2021

Global satellite observation surface albedo data over a long period of time are actively used to monitor changes in the global climate and environment, and their utilization and importance are great. Through the generational shift of geostationary satellites COMS (Communication, Ocean and Meteorological Satellite)/MI (Meteorological Imager sensor) and GK-2A (GEO-KOMPSAT-2A)/AMI (Advanced Meteorological Imager sensor), it is possible to continuously secure surface albedo outputs. However, the surface albedo outputs of COMS/MI and GK-2A/AMI differ between outputs due to Differences in retrieval algorithms. Therefore, in order to expand the retrieval period of the surface albedo of COMS/MI and GK-2A/AMI to secure continuous climate change monitoring linkage, the analysis of the two satellite outputs and errors should be preceded. In this study, error characteristics were analyzed by performing comparative analysis with ground observation data AERONET (Aerosol Robotic Network) and other satellite data GLASS (Global Land Surface Satellite) for the overlapping period of COMS/MI and GK-2A/AMI surface albedo data. As a result of error analysis, it was confirmed that the RMSE of COMS/MI was 0.043, higher than the RMSE of GK-2A/AMI, 0.015. In addition, compared to other satellite (GLASS) data, the RMSE of COMS/MI was 0.029, slightly lower than that of GK-2A/AMI 0.038. When understanding these error characteristics and using COMS/MI and GK-2A/AMI's surface albedo data, it will be possible to actively utilize them for long-term climate change monitoring.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1407-1422
• /
• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.12
• /
• pp.14-21
• /
• 2013

In satellite operations and space exploration missions, a ranging is one of the most essential technologies to get its navigational information of space probes. Recently, the importance of cross-support between space agencies is increasing for more fine performance of space mission. For cross-support, mutually compatible ranging system between space agencies is recommended. For these reasons, the consultative committee for space data systems (CCSDS) recommends pseudo noise (PN) ranging as a digital standard ranging system. The length of PN sequence in CCSDS standard is proper for deep space missions, however, it is too long to use for ranging in near earth missions. In this paper, we propose Variable Length PN sequence schemes suitable for ranging of near earth satellites, such as low-earth orbit (LEO), medium-earth orbit (MEO) and Geostationary orbit (GEO). Therefore we propose variable length PN sequence ranging system including CCSDS standard for multiple missions.

• Journal of Astronomy and Space Sciences
• /
• v.33 no.2
• /
• pp.127-135
• /
• 2016

Inactive space objects are usually rotating and tumbling as a result of internal or external forces. KOREASAT 1 has been inactive since 2005, and its drift trajectory has been monitored with the optical wide-field patrol network (OWL-Net). However, a quantitative analysis of KOREASAT 1 in regard to the attitude evolution has never been performed. Here, two optical tracking systems were used to acquire raw measurements to analyze the rotation period of two inactive satellites. During the optical campaign in 2013, KOREASAT 1 was observed by a 0.6 m class optical telescope operated by the Korea Astronomy and Space Science Institute (KASI). The rotation period of KOREASAT 1 was analyzed with the light curves from the photometry results. The rotation periods of the low Earth orbit (LEO) satellite ASTRO-H after break-up were detected by OWL-Net on April 7, 2016. We analyzed the magnitude variation of each satellite by differential photometry and made comparisons with the star catalog. The illumination effect caused by the phase angle between the Sun and the target satellite was corrected with the system tool kit (STK) and two line element (TLE) technique. Finally, we determined the rotation period of two inactive satellites on LEO and geostationary Earth orbit (GEO) with light curves from the photometry. The main rotation periods were determined to be 5.2 sec for ASTRO-H and 74 sec for KOREASAT 1.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1005-1005
• /
• 2003

The purpose of this study is to evaluate the potential application of TERRA-ASTER data in Thailand. ASTER VNIR, SWIR and TIR data covering greater Bangkok and Chiangmai province were processed with various techniques in the spatial domain to study the applicability to various disciplines. ASTER data was also combined with other satellite data in order to utilize multi-sensor methods. It was found that VNIR data can clearly identify urban pattern including road network and vegetation index. While SWIR and TIR data can well separate between urban and non urban area and TIR data can differentiate among thermal surfaces. Furthermore, dense urban areas such as central business area could be highlighted. Land utilization, vegetable distribution and differences of temperature distribution were investigated.