• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.25-28
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• 2016

Recently, because of the weather forecasts through the low-resolution data has been limited, the demand of the high-resolution data is sharply increasing. Therefore, in this study, we restore the ultra-high resolution synthetic precipitation and temperature data for 2000-2014 due to small-scale topographic effect using the QPM (Quantitative Precipitation Model)/QTM (Quantitative Temperature Model). First, we reproduce the detailed precipitation and temperature data with 1km resolution using the distribution of Automatic Weather System (AWS) data and Automatic Synoptic Observation System (ASOS) data, which is about 10km resolution with irregular grid over South Korea. Also, we recover the precipitation and temperature data with 1km resolution using the MERRA reanalysis data over North Korea, because there are insufficient observation data. The precipitation and temperature from restored current climate reflect more detailed topographic effect than irregular AWS/ASOS data and MERRA reanalysis data over the Korean peninsula. Based on this analysis, more detailed prospect of regional climate is investigated.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.6
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• pp.649-661
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• 2008

The impact of the detailed surface data on regional meteorological fields in complex coastal area is studied using RAMS. Resolutions of topography and land use data are very important to numerical modeling, because high resolution data can reflect correct terrain height and detail characteristics of the surface. Especially, in complex coastal region such as Gwangyang area, southern area in Korean Peninsula, high resolution topography and land use data are indispensable for accurate modeling results. This study investigated the effect of resolutions of terrain data using SRTM with 3 second resolution topography and KLU with 1 second resolution land use data. Case HR was the experiment using high resolution data, whereas Case LR used low resolution data. In Case HR, computed surface temperature was higher than Case LR along the coastline and wind speed was $1{\sim}2m/s$ weaker than Case LR. Time series of temperature and wind speed indicated great agreement with the observation data. Moreover, Case HR indicated outstanding results on statistical analysis such as regression, root mean square error, index of agreement.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.468-470
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• 2007

This paper is to present an approach for generating coarse resolution (MODIS data) fraction images of forested region in Korea peninsula using forest type area fraction derived from high resolution data (ASTER data) in regional forest area. A 15-m spatial resolution multi-spectral ASTER image was acquired under clear sky conditions on September 22, 2003 over the forested area near Seoul, Korea and was used to select each end-member that represent a pure reflectance of component of forest such as different forest, bare soil and water. The area fraction of selected each end-member and a 500-m spatial resolution MODIS reflectance product covering study area was applied to a linear mixture inversion model for calculating the fraction image of forest component across the South Korea. We found that the area fraction values of each end-member observed from high resolution image data could be used to separate forest cover in low resolution image data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.648-650
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• 2003

Efficient multi-resolution image fusion aims to take advantage of the high spectral resolution of Landsat TM images and high spatial resolution of SPOT panchromatic images simultaneously. This paper presents a multi-resolution data fusion scheme, based on multirate image representation. Motivated by analytical results obtained from high-resolution multispectral image data analysis: the energy packing the spectral features are distributed in the lower frequency bands, and the spatial features, edges, are distributed in the higher frequency bands. This allows to spatially enhancing the multispectral images, by adding the high-resolution spatial features to them, by a multirate filtering procedure. The proposed method is compared with some conventional methods. Results show it preserves more spectral features with less spatial distortion.

• Korean Journal of Remote Sensing
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• v.16 no.1
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• pp.87-98
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• 2000

Image merging techniques have been developed to integrate the advantage of different data type. The objective of this study is to present the optimal method for merging high spatial resolution panchromatic image, such as the latest commercial satellite data, and low spatial resolution mulitspectral images. For this study, a set of 2m resolution panchromatic and 8m resolution mulitspectral data were simulated by using airborne mulitspectral data. Five merging methods of MWD, IHS, PCA, HPF, and CN were applied to produce four bands of high spatial resolution mulitspectral data. Merging results were evaluated by visual interpretation, image statistics, semivariogram, and spectral characteristics. From the aspects of both spatial resolution and spectral information, the wavelet-based MWD merging method have shown very similar results compared with the original data used for the merging.

• Atmosphere
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• v.25 no.2
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• pp.309-322
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• 2015

The objective of this study is to evaluate the impact of the high resolution topographies and landuses data on simulated meteorological variables (wind speed at 10 m, temperature at 2 m and relative humidity at 2 m) in WRF. We compare the results with WRF simulation using each resolution of the topographies and landuses, and with 37 AWS observation data on the Seoul metropolitan regions. According to results of using high-resolution topography, WRF model gives better topographical expression over domain. And we can separate more detail (Low intensity residential, high intensity residential, industrial or commercial) using high resolution landuses data. The result shows that simulated temperature and wind speed are generally higher than AWS observation data. However, simulation trend with temperature, wind speed, and relative humidity are similar to observation data. The reason for that is that the high precipitation event occurred in CASE 1 and 2. Temperature have correlation of 0.43~0.47 and standard deviation of $2.12{\sim}2.28^{\circ}C$ in CASE 1, while correlation of more than 0.8 and standard deviation of $3.05{\sim}3.18m\;s^{-1}$ in CASE 2. In case of wind speed, correlation have lower than 0.5 and Standard Deviation of $1.88{\sim}2.34m\;s^{-1}$ in CASE 1 and 2. In statistical analysis shows that using highest resolution (U01) results are more close to the AWS observation data. It can be concluded that the topographies and landuses are important factor that affect model simulation. However, the tendency to always use high resolution topographies and landuses data appears to be unjustified, and optimal solution depends on the combination of scale effect and mechanisms of dynamic models.

• Current Industrial and Technological Trends in Aerospace
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• v.6 no.2
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• pp.90-98
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• 2008

In recent, image data compression method of high resolution satellite image is the important issue for its application and development. The CCSDS (Consultative Committee for Space Data Systems) published a standard for the high resolution image data compression, CCSDS 122.0-B-1, in the end of 2005, which is expected to be widely applied in process of compression for the high resolution satellite images. In this paper, it is explained that the current trends of image compression methods for high resolution satellites, and then the comparison results between CCSDS 122.0-B-1 standard and JPEG are described.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.217-223
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• 2014

With precise sensor position, attitude element, and imaging resolution, a simulated geospatial image can be generated. In this study, a satellite image is simulated using SPOT ortho-image and global elevation data, and the geometric similarity between original and simulated images is analyzed. Using a SPOT panchromatic image and high-density elevation data from a 1/5K digital topographic map data an ortho-image with 10-meter resolution was produced. The simulated image was then generated by exterior orientation parameters and global elevation data (SRTM1, GDEM2). Experimental results showed that (1) the agreement of the image simulation between pixel location from the SRTM1/GDEM2 and high-resolution elevation data is above 99% within one pixel; (2) SRTM1 is closer than GDEM2 to high-resolution elevation data; (3) the location of error occurrence is caused by the elevation difference of topographical objects between high-density elevation data generated from the Digital Terrain Model (DTM) and Digital Surface Model (DSM)-based global elevation data. Error occurrences were typically found at river boundaries, in urban areas, and in forests. In conclusion, this study showed that global elevation data are of practical use in generating simulated images with 10-meter resolution.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.85-92
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• 2022

In this paper, we propose a technique that can express low-resolution hair and fur simulations in high-resolution without noise using ConvNet and geometric images of strands in the form of lines. Pairs between low-resolution and high-resolution data can be obtained through physics-based simulation, and a low-resolution-high-resolution data pair is established using the obtained data. The data used for training is used by converting the position of the hair strands into a geometric image. The hair and fur network proposed in this paper is used for an image synthesizer that upscales a low-resolution image to a high-resolution image. If the high-resolution geometry image obtained as a result of the test is converted back to high-resolution hair, it is possible to express the elastic movement of hair, which is difficult to express with a single mapping function. As for the performance of the synthesis result, it showed faster performance than the traditional physics-based simulation, and it can be easily executed without knowing complex numerical analysis.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.63-70
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• 2021

Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.