• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.283-295
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• 2021

This paper proposes a method for generating dense depth map using information of color images and depth map generated based on lidar based on self-organizing map. The proposed depth map upsampling method consists of an initial depth prediction step for an area that has not been acquired from LiDAR and an initial depth filtering step. In the initial depth prediction step, stereo matching is performed on two color images to predict an initial depth value. In the depth map filtering step, in order to reduce the error of the predicted initial depth value, a self-organizing map technique is performed on the predicted depth pixel by using the measured depth pixel around the predicted depth pixel. In the process of self-organization map, a weight is determined according to a difference between a distance between a predicted depth pixel and an measured depth pixel and a color value corresponding to each pixel. In this paper, we compared the proposed method with the bilateral filter and k-nearest neighbor widely used as a depth map upsampling method for performance comparison. Compared to the bilateral filter and the k-nearest neighbor, the proposed method reduced by about 6.4% and 8.6% in terms of MAE, and about 10.8% and 14.3% in terms of RMSE.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1257-1271
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• 2022

Recently, many Earth observation optical satellites have been developed, as their demands were increasing. Therefore, a rapid preprocessing of satellites became one of the most important problem for an active utilization of satellite images. Satellite image matching is a technique in which two images are transformed and represented in one specific coordinate system. This technique is used for aligning different bands or correcting of relative positions error between two satellite images. In this paper, we propose an automatic image matching method among satellite images with different ground sampling distances (GSDs). Our method is based on improved feature matching and robust estimation of transformation between satellite images. The proposed method consists of five processes: calculation of overlapping area, improved feature detection, feature matching, robust estimation of transformation, and image resampling. For feature detection, we extract overlapping areas and resample them to equalize their GSDs. For feature matching, we used Oriented FAST and rotated BRIEF (ORB) to improve matching performance. We performed image registration experiments with images KOMPSAT-3A and RapidEye. The performance verification of the proposed method was checked in qualitative and quantitative methods. The reprojection errors of image matching were in the range of 1.277 to 1.608 pixels accuracy with respect to the GSD of RapidEye images. Finally, we confirmed the possibility of satellite image matching with heterogeneous GSDs through the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.359-364
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• 2022

The change detection in remote sensing results in errors due to the camera's optical factors, seasonal factors, and land cover characteristics. The inclination of the building in the image was simulated according to the camera angle using the Cycle Generative Adversarial Network method, and the simulated image was used to contribute to the improvement of change detection accuracy. Based on CycleGAN, the inclination of the building was similarly simulated to the building in the other image based on the image of one of the two periods, and the error of the original image and the inclination of the building was compared and analyzed. The experimental data were taken at different times at different angles, and Kompsat-3A high-resolution satellite images including urban areas with dense buildings were used. As a result of the experiment, the number of incorrect detection pixels per building in the two images for the building area in the image was shown to be reduced by approximately 7 times from 12,632 in the original image and 1,730 in the CycleGAN-based simulation image. Therefore, it was confirmed that the proposed method can reduce detection errors due to the inclination of the building.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.283-292
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• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

• Journal of Korean Ophthalmic Optics Society
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• v.9 no.2
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• pp.431-437
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• 2004

This research reviewed the objects of 384 persons (male:86, female:298) from 6 to 50 years old visited the S. Optical shop at Mokpo over two times from Mar. 2002 to Feb. 2004. We obtained the following results by analyzing the visual acuity prescription to keeping at S. Optical shop. 1. The abnormal refraction eyes were 191 persons(49.74%) for simple myopia, 2 persons(0.52%) for myopic simple astigmatism, 180 persons(46.88%) for myopic compound astigmatism, and 1 person(0.26%) for simple hyperopia, 2 persons(0.52%) for hyperopic simple astigmatism, 8 persons(2.08%) for hyperopic compound astigmatism, respectively. 2. Classifying of correction power for 373 persons (male:84, female:289) myopia, so that showed 166 persons(male:36, female) between $$0.25D{\leq_-}2.00D$$, 194 persons(male:46, female:148) between $$2.25D{\leq_-}6.00D$$, 13 persons(male:2, female:11) for over 6.250 respectively. 3. According to the kinds of 192 persons astigmatism subjects(male:53, female:148), direct astigmatism was 145 persons(male:32, female:113), oblique astigmatism 33 persons (male:12, female:21), reverse astigmatism 14 persons(male:5, female:9). 4. The variation of spherical power for myopia showed 299 persons(male:71, female:228) between $$0.00D{\leq_-}0.50D$$, 64 persons(male:11, female:53) between $$0.51D{\leq_-}1.00D$$, 9 persons(male:2, female:7) between $$1.01D{\leq_-}1.50D$$, 1 person(male:0, female:1) between $$1.51D{\leq_-}2.00D$$ variation respectively. Hyperopia showed 8 persons(male:1, female:7) between $$0.00D{\leq_-}0.50D$$, 3 persons(male:1, female:2) between $$0.51D{\leq_-}1.00D$$ variation respectively. 5. The variation of astigmatism power showed 181 persons(male:48 female:113) between $$0.00D{\leq_-}0.25D$$, 25 persons(male:9, female:16) between $$0.26D{\leq_-}0.50D$$, 6 persons(male:0, female:6) between $$0.51D{\leq_-}0.75D$$ astigmatism variation respectively.

• Journal of Korean Ophthalmic Optics Society
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• v.8 no.2
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• pp.169-175
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• 2003

This research reviewed that 83 male subjects. 89 female subjects of middle and high school visited D Optical shop at the downtown of Daegu more than twice from January, 1999 to January, 2003 and obtained the following results by using the visual acuity prescription of them for which D Optical shop was keeping. 1. The classification of correction power for 190 myopia eyes was examined (87 male eyes, 103 female eyes) showed 89 eyes(46.82%) between $0.25D{\leq}3.00D$, 86 eyes(45.26%) between $3.25D{\leq}6.00D$, 15 eyes(7.89%) for over 6.25D. 2. The kind of 154 astigmatism subjects(79 male eyes, 75 female eyes) was direct astigmatism 83.77%, reverse astigmatism 11.69%, oblique astigmatism 4.55%. The cylindrical correction power for astigmatic eyes was 61 eyes(39.61%) between $0.25D{\leq}0.50D$, 60 eyes(38.96%) between 0.50D<1.00D, 121 eyes(78.57%) for less than 1.06D, 6 eyes(0.65%) for over 3.00D. 3. The variation of spherical power showed 161 eyes(46.80%) between $0.00D{\leq}0.50D$, 109 eyes(31.69%) between $0.51D{\leq}1.00D$, 17 eyes(4.94%) for over 2.01D variation. 4. The variation of astigmatic power showed 92 eyes(59.74%) between $0.00D{\leq}0.50D$, 39 eyes(25.32%) between $0.26D{\leq}0.50D$, 10eyes (6.49%) between $0.51D{\leq}0.75D$, 13 eyes(8.44 %) for over 0.76D astigmatic variation. 5. The variation of equivalent spherical power showed 137 eyes(39.83%) between $0.00D{\leq}0.50D$, 126 eyes(36.63%) between $0.51D{\leq}1.00D$, 40 eyes(11.63%) between $1.01D{\leq}1.50D$, 21 eyes(6.10%) between $1.51D{\leq}2.00D$, 20 eyes(5.81%) for over 2.01D variation.

• Journal of Radiation Protection and Research
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• v.20 no.1
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• pp.45-52
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• 1995

This study is to compare A point doses in human pelvic phantom by film dosimetry, computer planning and manual calculation by using of along-away table. We developed tissue equivalent human pelvic phantom composed of four pieces of cylindrical acryl tubes with water, to simulate intracavitary radiation (ICR) in patients with cervix cancer. When the phantom assembled from 4 pieces, it has a small space for inserting Fletcher-Suit-Delclos applicator like a human vagina. Fletcher-Suit-Delclos applicator inserted into the space was packed tightly with furacin gauzes, and three $^{137}Cs$ sources with radioactivity of $15.7mg\;Ra-eq$ were inserted into the tandem. For the film dosimetry, two pieces of X-OMAT V film (Kodak Co.) of which planes include point A, were arranged orthogonally in the slits between phantoms. A point dose and iso-dose curves were measured by means of optical densitometer. A point doses by film dosimetry, RTP system and manual calculation by using of along-away table were compared, and iso-dose curves by film dosimetry and computer planning were also compared. The dose of A point was 51.2cGy/hr by film dosimetry, 46.7cGy/hr by RTP system and 47.9 cGy/hr by along-away table. A point dose by computer planning was similar to the dose by calculation using of along-away table with acceptable accuracy $({\pm}3%)$, however, the dose by film dosimetry was different from two others with about 10% error. Since most clinical beams contains a scatter component of low energy photons, the correlation between optical density and dose becomes tenuous. In addition, film suffers from several potential errors such as changes in processing conditions, interfilm emulsion differences, and artifacts caused by air pockets adjacent to the film. For these reasons, absolute dosimetry with film is impractical, however, it is very useful for checking qualitative patterns of a radiation distribution. In future, solid state dosimeter such as TLD must be used for the dosimetry of ionizing radiation. When considerable care is used, precision of approximately 3% may be obtained using TLD.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Journal of The Korean Society of Grassland and Forage Science
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• v.26 no.1
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• pp.53-62
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• 2006

Near infrared reflectance spectroscopy (NIRS) has been increasingly used as a rapid, accurate method of evaluating some chemical compositions in forages. Analysis of forage quality by NIRS usually involves dry ground samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations and spectral math treatments on prediction ability of chemical composition for corn silage by NIRS. A population of 112 corn silage representing a wide range in chemical parameters were used in this investigation. Samples of com silage were scanned at 2nm intervals over the wavelength range 400-2500nm and the optical data recorded as log l/Reflectance(log l/R) and scanned in overt-dried grinding(ODG), liquid nitrogen grinding(LNG) or intact fresh(IF) condition. Samples were analysed for neutral detergent fiber(NDF), acid detergent fiber(ADF), acid detergent lignin(ADL), crude protein(CP) and crude ash content were expressed on a dry-matter(DM) basis. The spectral data were regressed against a range of chemical parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with four spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation(SECV). The results of this study show that NIRS predicted the chemical parameters with very high degree of accuracy(the correlation coefficient of cross validation$(R^2cv)$ range from $0.70{\sim}0.95$) in ODG. The optimum equations were selected on the basis of minimizing the standard error of prediction(SEP). The Optimum sample preparation methods and spectral math treatment were for ADF, the ODG method using 2,10,5 math treatment(SEP = 0.99, $R^2v=0.93$), and for CP, the ODG method using 1,4,4 math treatment(SEP = 0.29. $R^2v=0.91$).

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.959-974
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• 2021

Forel-Ule Index (FUI) is an index which classifies the colors of inland and seawater exist in nature into 21 gradesranging from indigo blue to cola brown. FUI has been analyzed in connection with the eutrophication, water quality, and light characteristics of water systems in many studies, and the possibility as a new water quality index which simultaneously contains optical information of water quality parameters has been suggested. In thisstudy, Ocean Colour-Climate Change Initiative (OC-CCI) based 4 km FUI was spatially downscaled to the resolution of 500 m using the Geostationary Ocean Color Imager (GOCI) data and Random Forest (RF) machine learning. Then, the RF-derived FUI was examined in terms of its correlation with various water quality parameters measured in coastal areas and its spatial distribution and seasonal characteristics. The results showed that the RF-derived FUI resulted in higher accuracy (Coefficient of Determination (R²)=0.81, Root Mean Square Error (RMSE)=0.7784) than GOCI-derived FUI estimated by Pitarch's OC-CCI FUI algorithm (R²=0.72, RMSE=0.9708). RF-derived FUI showed a high correlation with five water quality parameters including Total Nitrogen, Total Phosphorus, Chlorophyll-a, Total Suspended Solids, Transparency with the correlation coefficients of 0.87, 0.88, 0.97, 0.65, and -0.98, respectively. The temporal pattern of the RF-derived FUI well reflected the physical relationship with various water quality parameters with a strong seasonality. The research findingssuggested the potential of the high resolution FUI in coastal water quality management in the Korean Peninsula.