• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.571-586
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• 2014

Moderate Resolution Imaging Spectroradiometer (MODIS) provides air temperature (Tair) and dew point temperature (Tdew) profiles at a spatial resolution of 5 km. New Collection 6 (C006) MODIS07_L2 atmospheric profile product has been produced since 2012. The Collection 6 algorithm has several modifications from the previous Collection 5 (C005) algorithm. This study evaluated reliabilities of two alternative datasets of surface-level Tair and Tdew derived from C005 and C006 Aqua MODIS07_L2 (MYD07_L2) products using ground measured temperatures from 77 National Weather Stations (NWS). Saturated and actual vapor pressures were calculated using MYD07_L2 Tair and Tdew. The C006 Tair showed lower mean error (ME, -0.76 K) and root mean square error (RMSE, 3.34 K) than the C005 Tair (ME = -1.89 K, RMSE = 4.06 K). In contrasts, ME and RMSE of C006 Tdew were higher than those (ME = -0.39 K, RMSE = 5.65 K) of C005 product. Application of ambient lapse rate for Tair showed appreciable improvements of estimation accuracy for both of C005 and C006, though this modification slightly increased errors in C006 Tdew. The C006 products provided better estimation of vapor pressure datasets than the C005-derived vapor pressure. Our results indicate that, except for Tdew, C006 MYD07_L2 product showed better reliability for the region of South Korea than the C005 products.

• Journal of Family Relations
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• v.20 no.4
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• pp.47-72
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• 2016

According to the survey by the Ministry of Unification, 28,133 live in South Korea as of september 2015 and the number has been steadily increased. It is interesting to note that the majority of defectors were women. Statistics say that the total of women defectors was 1,138 in 2002 and consisted of 55.5%. But the number increased to 2,706 in 2011 and consisted of 70.5%(Wikipedia, 2015). Most of them have trauma, because they experienced terrible events like prostitution, sexual abuse, domestic violence, etc. while they were living in South Korea. They had to get over a lot of challenges they never expected. They had to do whatever they need to do for their survival in South Korea as well as in North Korea. Objectives: This study focuses on identifying the psychosocial resources available for them in order to help them out living here. Method: For this purpose, this study conducted unstructured in-depth interviews with four women defectors from North Korea. Their age bracket is between the ages of 50 and 58 and they have stayed over five years in South Korea. They were recommended by social welfare workers, who had dealt with them over 20 years. For the reliability of this study, the three methods were performed such as three-month close relationship, three-person triangular verification, and diversification of resources-the field notes and observation notes. Results: As a result of this study, the resources may be labeled into three main clusters of themes such as 1) Self-Preservation - 'Self-Love', 'Family-Attachment', 'Share the Goods', 'Live in Harmony', 'Avoidance of conflict'; 2) Self-Conquest - 'Endurance', 'Earnestness and Effort', 'My own Activities', 'Chat', 'Put Down', 'Appreciation and Optimism'; 3) Self-Presence Awareness - 'I was a Precious Daughter', 'I am Mother'. Also there are thirteen sub categories to be considered. Conclusions: This study focuses on identifying how women defectors have used or can use the psychosocial resources available for them, rather than what they have struggled with. It is meaningful to identify in a positive light that this study provides how they have adjusted to a totally different context for their survival and what psychosocial resources they have used. This study can give an idea about how they may receive proper and practical help from the government programs in order to become better used to new living circumstances in South Korea. As well, this study can provide meaningful criteria and guidance with family life educators for women defectors and social workers working in counseling and social welfare areas. This study contributes to better understanding about how they are using these psychosocial resources.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.13-23
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• 2021

This study intended to assess the reliability of topographic data using satellite imaging data. The topographical data using actual instrumentation data and satellite image data were established and applied to the rainfall-leak model, S-RAT, and the topographical data and outflow data were compared and analyzed. The actual measurement data were collected from the Water Resources Management Information System (WAMIS), and satellite image data were collected from MODIS observation sensors mounted on Terra satellites. The areas subject to analysis were selected for two rivers with more than 80% mountainous areas in the Han River basin and one river basin with more than 7% urban areas. According to the analysis, the difference between instrumentation data and satellite image data was up to 50% for peak floods and up to 17% for flood totals in rivers with high mountains, but up to 13% for peak floods and up to 4% for flood totals. The biggest difference in the video data is Landuse, which shows that MODIS satellite images tend to be recognized as cities up to 60% or more in urban streams compared to WAMIS instrumentation data, but MODIS satellite images are found to be less than 5% error in forest areas.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.533-544
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• 2018

Rainfall is one of the most important meteorological variables in meteorology, agriculture, hydrology, natural disaster, construction, and architecture. Recently, satellite remote sensing is essential to the accurate detection, estimation, and prediction of rainfall. In this study, the accuracy of Integrated Multi-satellite Retrievals for GPM (IMERG) product, a composite rainfall information based on Global Precipitation Measurement (GPM) satellite was evaluated with ground observation data in the inland of Korea. The Automatic Weather Station (AWS)-based rainfall measurement data were used for validation. The IMERG and AWS rainfall data were collocated and compared during one year from January 1, 2016 to December 31, 2016. The coastal regions and islands were also evaluated irrespective of the well-known uncertainty of satellite-based rainfall data. Consequently, the IMERG data showed a high correlation (0.95) and low error statistics of Bias (15.08 mm/mon) and RMSE (30.32 mm/mon) in comparison to AWS observations. In coastal regions and islands, the IMERG data have a high correlation more than 0.7 as well as inland regions, and the reliability of IMERG data was verified as rainfall data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.376-385
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• 2018

To estimate the hourly temperature distribution due to solar radiation during the day, on slope in complex terrain, an empirical formula was developed including the hourly deviation in the observed temperature following solar radiation deviation, at weather stations on the east-facing and west-facing slopes. The solar radiation effect was simulated using the empirical formula to estimate hourly temperature at 11 weather observation sites in mountainous agricultural areas, and the result was verified for the period from January 2015 to December 2017. When the estimated temperature was compared with the control, only considering temperature lapse rate, it was found that the tendency to underestimate the temperature from 9 am to 3 pm was reduced with the use of an empirical formula in the form of linear expression; consequently, the estimation error was reduced as well. However, for the time from 5 pm to 6 pm, the estimation error was smaller when a hyperbolic equation drawn from the deviation in solar radiation on the slope, which was calculated based on geometric conditions, was used instead of observed values. The reliability of estimating the daytime temperature at 3 pm was compared with existing estimation model proposed in other studies; the estimation error could be mitigated up to an ME (mean error) of $-0.28^{\circ}C$ and RMSE (root mean square error) of $1.29^{\circ}C$ compared to the estimation error in previous models (ME $-1.20^{\circ}C$, RMSE $2.01^{\circ}C$).

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.1
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• pp.49-61
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• 2021

The repeated shipboard measurements that have been conducted by the National Institute of Fisheries Science (NIFS) for more than a half century, provide the valuable long-term hydrographic data with high spatial-temporal resolution. However, this unprecedent dataset has been rarely used for oceanic climate sciences because of its reliability issue. In this study, temporal variability of salinity error in the NIFS data was quantified by means of extremely small variability of salinity in the deep layer of the south-western East Sea, in order to contribute to studies on long-term variability of the East Sea. The NIFS salinity errors estimated on the isothermal surfaces of 1℃ have a remarkable temporal variation, such as ~0.160 g/kg in the year of 1961~1980, ~0.060 g/kg in 1981~1994,~0.020 g/kg in 1995~2002, and ~0.010 g/kg in 2003~2014 on average, which basically represent bias error. In the recent years, even though the quality of salinity has been improved, there still remain relatively large bias errors in salinity data presumably due to failure of salinity sensor managements, especially in 2011, 2013, and 2014. On the contrary, the salinity in the year of 2012 was very accurate and stable, whose error was estimated as about 0.001 g/kg comparable to the salinity sensor accuracy. Thus, as long as developing proper data quality control procedures and sensor management systems, I expect that the NIFS shipboard hydrographic data could have good enough quality to support various studies on ocean response to climate variabilities. Additionally, a few points to improve the current NIFS shipboard measurements were suggested in the discussion section.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.319-325
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• 2022

The Study In order to obtain a sharpness Image from Skull PA axial projection (Haas) in a head axial X-ray Examination, this study changed the posture angle using Skull Phantom and evaluated the image subjectively to 5 radiologists who worked in the Department of Imaging at University Hospital. In the prone position, the head was lowered 4 cm from the back of the head, entered 25° toward the head, and the image evaluation score was high with 20 points, such as the back bone, dorsum sellae projected in the large hole, and posterior clinoid process. In addition, the score significance was verified, and the Cronbach Alpha value was evaluated to have good reliability of 0.789. As a result of calculating the signal-to-noise ratio (SNR) by setting the region of interest (ROI) of the image, it was the highest at 5.957 for 25° incident at the back of the head and 6.430 for 30° incident at the back of the head. As a result of the study, in order to obtain a sharp image of the back of the head bone, dorsum sellae, and posterior clinoid process when shooting in the axial direction after the head, it is filmed by tilting 25° toward the head from 4 cm below the back of the head. In order to obtain a sharp image of rock pyramid symmetry, petrous ridge, sagittal suture, and lambdoid suture, it is thought that it will be helpful for clinical use if you shoot it 8cm down from the back of the head and tilt it 30° toward the head.

• Journal of Space Technology and Applications
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• v.2 no.1
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• pp.41-51
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• 2022

CubeSat is a satellite platform that is widely used not only for earth observation but also for space exploration. CubeSat is also used in magnetic field investigation missions to observe space physics phenomena with various shape configurations of magnetometer instrument unit. In case of magnetic field measurement, the magnetometer instrument should be far away from the satellite body to minimize the magnetic disturbances from satellites. But the accommodation setting of the magnetometer instrument is limited due to the volume constraint of small satellites like a CubeSat. In this paper, we investigated that the magnetic field interference generated by the cube satellite was analyzed how much it can affect the reliability of magnetic field measurement. For this analysis, we used a reaction wheel and Torque rods which have relatively high-power consumption as major noise sources. The magnetic dipole moment of these parts was derived by the data sheet of the manufacturer. We have been confirmed that the effect of the residual moment of the magnetic torque located in the middle of the 3U cube satellite can reach 36,000 nT from the outermost end of the body of the CubeSat in a space without an external magnetic field. In the case of accurate magnetic field measurements of less than 1 nT, we found that the magnetometer should be at least 0.6 m away from the CubeSat body. We expect that this analysis method will be an important role of a magnetic cleanliness analysis when designing a CubeSat to carry out a magnetic field measurement.