• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.871-884
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• 2021

The purpose of this study was to determine ways to increase efficiency in constructing and verifying artificial intelligence learning data on land cover using aerial and satellite images, and in applying the data to AI learning algorithms. To this end, multi-resolution datasets of 0.51 m and 10 m each for 8 categories of land cover were constructed using high-resolution aerial images and satellite images obtained from Sentinel-2 satellites. Furthermore, fine data (a total of 17,000 pieces) and coarse data (a total of 33,000 pieces) were simultaneously constructed to achieve the following two goals: precise detection of land cover changes and the establishment of large-scale learning datasets. To secure the accuracy of the learning data, the verification was performed in three steps, which included data refining, annotation, and sampling. The learning data that wasfinally verified was applied to the semantic segmentation algorithms U-Net and DeeplabV3+, and the results were analyzed. Based on the analysis, the average accuracy for land cover based on aerial imagery was 77.8% for U-Net and 76.3% for Deeplab V3+, while for land cover based on satellite imagery it was 91.4% for U-Net and 85.8% for Deeplab V3+. The artificial intelligence learning datasets on land cover constructed using high-resolution aerial and satellite images in this study can be used as reference data to help classify land cover and identify relevant changes. Therefore, it is expected that this study's findings can be used in the future in various fields of artificial intelligence studying land cover in constructing an artificial intelligence learning dataset on land cover of the whole of Korea.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.777-788
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• 2021

Since aerosol has a relatively short duration and significant spatial variation, satellite observations become more important for the spatially and temporally continuous quantification of aerosol. However, optical remote sensing has the disadvantage that it cannot detect AOD (Aerosol Optical Depth) for the regions covered by clouds or the regions with extremely high concentrations. Such missing values can increase the data uncertainty in the analyses of the Earth's environment. This paper presents a spatial gap-filling framework using a univariate statistical method such as DCT-PLS (Discrete Cosine Transform-based Penalized Least Square Regression) and FMM (Fast Matching Method) inpainting. We conducted a feasibility test for the hourly AOD product from AHI (Advanced Himawari Imager) between January 1 and December 31, 2019, and compared the accuracy statistics of the two spatial gap-filling methods. When the null-pixel area is not very large (null-pixel ratio < 0.6), the validation statistics of DCT-PLS and FMM techniques showed high accuracy of CC=0.988 (MAE=0.020) and CC=0.980 (MAE=0.028), respectively. Together with the AI-based gap-filling method using extra explanatory variables, the DCT-PLS and FMM techniques can be tested for the low-resolution images from the AMI (Advanced Meteorological Imager) of GK2A (Geostationary Korea Multi-purpose Satellite 2A), GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI2 (Geostationary Ocean Color Imager) of GK2B (Geostationary Korea Multi-purpose Satellite 2B) and the high-resolution images from the CAS500 (Compact Advanced Satellite) series soon.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.107-118
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• 2014

The objective of this study is to suggest a method for estimating rainfall-runoff relationship using runoff analysis with satellite rainfall and global geographic data for the region due to lack of observed data. This study uses CMORPH and GSMaP_NRT as satellite rainfall data, and GTOPO30 and GLCC as global geographic data. IFAS is used for runoff modeling. In the evaluation of rainfall data, the correlation coefficients of CMORPH and GSMaP_NRT with observed data are 0.37 and 0.30 respectively. Calculated peak runoffs using IFAS show small relative errors with observed data in case of parameters are not calibrated with satellite rainfall data. Therefore, the methods suggested in this study could be applied to ungauged watershed. In the future, this study will analyze runoff for North Korea, a representative inaccessible region, using satellite rainfall and global geographic data.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1618-1622
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• 2004

In this paper, the linearly interpolated PRC(Pseudo Range Correction) regenerating algorithm was applied to improve the DGPS(Differential Global Positioning System) positioning accuracy at user's spot by using the various PRC information obtained from multi-DGPS reference stations. The PRC information of each GPS satellite is not varying rapidly; it is possible to assume that the variation of PRC information of each GPS satellite is linear. So the linearly interpolated PRC regenerating algorithm can be applied to improve the DGPS positioning accuracy at user's spot by using the various PRC information obtained from multi-DGPS reference stations. To test the performance of the linearly interpolated PRC regenerating algorithm, maritime DGPS reference stations' PRC data was used in RTCM format. 11 maritime DGPS reference stations are in service providing DGPS information to public since 1999. Two set of 3 DGPS reference stations are selected to compare the performance of the linearly interpolated PRC regenerating algorithm. The DGPS positioning accuracy was dramatically improved about 40%. Linearly interpolated PRC regenerating algorithm adopted multi-channel DGPS receiver will be developed in near future.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.44-46
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• 2003

In 1996, Thailand's participation in the Pacific Rim as a part of NASA's Mission to Planet Earth (MTPE) Program, was titled 'AIRSAR Thailand Project'. In this project the Department of Land Development utilized Topographic SAR (TOPSAR) which had multi-frequencies: C band, L band, and P band with multi-polarization: HH, VV, and HV as well as C band VV DEM. Satellite data such as LANDSAT TM was also utilized for optimal use. Results of AIRSAR image processing including data fusion among difference wavelength bands and polarization revealed the quality of AIRSAR that best suit for detection of agricultural land uses. The HH-L band AIRSAR was proven to be useful to distinguish among crop types when combined with appropriate data. The HH, VV, and HV-P band enhanced surface characteristics of swamp forest and wetland. In addition, TOPSAR has its great advantage for identification of salt farms and shrimp ponds.

• Aerospace Engineering and Technology
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• v.2 no.2
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• pp.89-95
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• 2003

In this study, the technical characteristics of geosynchronous multi-mission satellites are investigated, compared to communication satellites. Geosynchronous meteorological satellites, whose imaging data is normally shared with the international society, have large coverage for monitoring and data service. Also the higher pointing accuracy is requested to keep the spatial resolution of 1-4km, compared to those of communication satellites. Cryogenic thermal control is needed for the better performance of IR sensors and the contamination protection of optical parts should be considered. On the other hands, for the successful development of the multi-mission satellite COMS, which will be launched in 2008, the special features of attitude control, electrical power, thermal control and mechanism are investigated.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.530-532
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• 2007

Multi-Spectral Camera(MSC) is a main payload on the KOMPSAT-2 satellite to perform the earth remote sensing. The MSC instrument has one(1) channel for panchromatic imaging and four(4) channel for multi-spectral imaging covering the spectral range from 450nm to 900nm using TDI CCD Focal Plane Array (FPA). The compression method on KOMPSAT-2 MSC was selected and used to match EOS input rate and PDTS output data rate on MSC image data chain. At once the MSC performance was carefully handled to minimize any degradation so that it was analyzed and restored in KGS(KOMPSAT Ground Station) during LEOP and Cal./Val.(Calibration and Validation) phase. In this paper, on-orbit image data chain in MSC and image data processing on KGS including general MSC description is briefly described. The influences on image performance between on-board compression algorithms and between performance restoration methods in ground station are analyzed and discussed.

• Aerospace Engineering and Technology
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• v.4 no.1
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• pp.211-220
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• 2005

The purpose of this study is to examine the image-based atmospheric correction models using the data from Landsat Enhanced Thermal Mapper Plus (ETM+) that have quite similar spectral characteristics to the forthcoming KOrea Multi-Purpose SATellite (KOMPSAT)-2 Multi-Spectral Camera (MSC), and the in-situ measured surface reflectance data during satellite overflight. The main advantage of this type of correction is that it does not require in-situ measurements during each satellite overflight. While substantial differences are present between Top-Of-the Atmosphere (TOA) reflectance and in-situmeasurements, the results showed that Case 1 based on COST model gives most accurate results among three cases. The accuracy of Case 2_1 is very close to Case 1 and its values are smaller than in-situ data. No notable features appear between some bands in the Case 3_1 and in-situ data. It is expected from this study that if the current methods are applied to the IKONOS high resolution data, we will be able to develop the suitable atmospheric correction methods for MSC data.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.6
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• pp.556-563
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• 2007

The current trend of low cost ultra-small satellites is to utilize Commercial Off the Shelf (COTS) parts to save cost, and accordingly, Command and Data Handling (C&DH) that operates the satellite and collects/processes the data is also designed and developed around commercial controllers. However, functionalities of commercial controllers are limited according to the specs outlined by the manufacturer. In order for the commercial controllers to be used for satellites where variety of interfaces is required, a separate interface circuit is required. Therefore, a Multi Digital Data Control System (MDDCS) using Field Programmable Gate Array (FPGA) has been developed in order to expand multiple digital interfaces that are not supported by the commercial controller, and also to compensate for SEU. This has been implemented on Actel A3P1000 using Very High Speed Integrated Circuits Hardware Description Language (VHDL).