• Bulletin of the Korean Space Science Society
• /
• 2008.10a
• /
• pp.31.4-32
• /
• 2008

The microwave instruments are used many areas of the space remote sensing and space science applications. The imaging radar of synthetic aperture radar (SAR) is well known microwave radar sensor for earth surface and ocean research. Unlike radar, microwave radiometer is passive instrument and it measures the emission energy of target, i.e. brightness temperature BT, from earth surface and atmosphere. From measured BT, the geophysical data like cloud liquid water, water vapor, sea surface temperature, surface permittivity can be retrieved. In this paper, the radiometer characteristics, system configuration and principle of BT measurement are described. Also the radiometer instruments TRMM, GPM, SMOS for earth climate, and ocean salinity research are introduce. As first korean microwave payload on STSAT-2, the DREAM (Dual-channels Radiometer for Earth and Atmosphere Monitoring) is described the mission, system configuration and operation plan for life time of two years. The main issues of DREAM unlike other spaceborne radiometers, will be addressed. The calibration is the one of main issues of DREAM mission and how it contribute on the space borne radiometer. In conclusion, the radiometer instrument to space science application will be considered.

• Korean Journal of Remote Sensing
• /
• v.23 no.6
• /
• pp.501-506
• /
• 2007

Basic research is conducted to identify a target using corner reflectors which are commonly used in calibration of synthetic aperture radar (SAR) systems. At first, an omni-directional reflector is fabricated by combining four 15-cm rectangular trihedral corner reflectors. Then, its radar cross section (RCS) characteristics are measured at C-band (5.3 GHz) for vv-, hh-, hv-, and vh- polarizations at a range of horizontal angle, $-90^{\circ}{\le}{\phi}{\le}90^{\circ}$. The measured RCS angular variation of the omni-directional reflector is much smaller for vv-polarization than other polarizations, and the difference between the maximum and minimum RCSs for vv-polarization is about 8 dB. Peak RCS values are shown at $0^{\circ}$ (normal to plates) and $45^{\circ}$ (direction of bore sight). It is shown that the measurements agree quite well with numerical simulation and theoretical computation results.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.6
• /
• pp.1-13
• /
• 2021

In this study, a time series analysis technique was applied to calibrate and predict traffic data for various purposes, such as planning, design, maintenance, and research. Existing algorithms have limitations in application to data such as traffic data because they show strong periodicity and seasonality or irregular data. To overcome and supplement these limitations, we applied the SARIMA model, an analytical technique that combines the autocorrelation model, the Seasonal Auto Regressive(SAR), and the seasonal Moving Average(SMA). According to the analysis, traffic volume prediction using the SARIMA(4,1,3)(4,0,3) 12 model, which is the optimal parameter combination, showed excellent performance of 85% on average. In addition to traffic data, this study is considered to be of great value in that it can contribute significantly to traffic correction and forecast improvement in the event of missing traffic data, and is also applicable to a variety of time series data recently collected.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1141-1180
• /
• 2022

The application of satellite imageries has increased in the field of hydrology and water resources in recent years. However, challenges have been encountered on obtaining accurate evapotranspiration and soil moisture. Therefore, present researches have emphasized the necessity to obtain estimations of satellite-based evapotranspiration and soil moisture with related development researches. In this study, we presented the research status in Korea by investigating the current trends and methodologies for evapotranspiration and soil moisture. As a result of examining the detailed methodologies, we have ascertained that, in general, evapotranspiration is estimated using Energy balance models, such as Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapotranspiration with Internalized Calibration (METRIC). In addition, Penman-Monteith and Priestley-Taylor equations are also used to estimate evapotranspiration. In the case of soil moisture, in general, active (AMSR-E, AMSR2, MIRAS, and SMAP) and passive (ASCAT and SAR)sensors are used for estimation. In terms of statistics, deep learning, as well as linear regression equations and artificial neural networks, are used for estimating these parameters. There were a number of research cases in which various indices were calculated using satellite-based data and applied to the characterization of drought. In some cases, hydrological cycle factors of evapotranspiration and soil moisture were calculated based on the Land Surface Model (LSM). Through this process, by comparing, reviewing, and presenting major detailed methodologies, we intend to use these references in related research, and lay the foundation for the advancement of researches on the calculation of satellite-based hydrological cycle data in the future.

• Korean Journal of Computational Design and Engineering
• /
• v.19 no.4
• /
• pp.356-367
• /
• 2014

A robotic sapatial augmented reality (RSAR) system, which combines robotic components with projector-based AR technique, is unique in its ability to expand the user interaction area by dynamically changing the position and orientation of a projector-camera unit (PCU). For a moving PCU mounted on a conventional robotic device, we can compute its extrinsic parameters using a robot kinematics method assuming a link and joint geometry is available. In a RSAR system based on user-created robot (UCR), however, it is difficult to calibrate or measure the geometric configuration, which limits to apply a conventional kinematics method. In this paper, we propose a data-driven kinematics control method for a UCR-based RSAR system. The proposed method utilized a pre-sampled data set of camera calibration acquired at sufficient instances of kinematics configurations in fixed joint domains. Then, the sampled set is compactly represented as a set of B-spline surfaces. The proposed method have merits in two folds. First, it does not require any kinematics model such as a link length or joint orientation. Secondly, the computation is simple since it just evaluates a several polynomials rather than relying on Jacobian computation. We describe the proposed method and demonstrates the results for an experimental RSAR system with a PCU on a simple pan-tilt arm.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1357-1369
• /
• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.