• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.40 no.9
• /
• pp.807-817
• /
• 2012

An optical payload needs to be validated its image performance after launched into orbit. The image performance was validated by observing star because ground site contains uncertainties caused by atmosphere, time of the year, and weather. Time Delayed and Integration(TDI) technique, which is mostly used to observe the ground, is going to be used to observe the selected stars. A satellite attitude scenario was also developed to observe the selected stars. The scenario is created to enable TDI to operate. Rotation angles of optical payload are determined in order for the selected stars to properly be passed at a desired angular velocity about rotation axis. The result of this research can be utilized to validate the quality of optical payload of a satellite in orbit. In addition, a quaternion for pointing selected stars is calculated minimizing the path from a given arbitrary attitude of satellite.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.750-750
• /
• 2002

The Airship PKNU is a roughly 12 m (32 ft) long blimp, filled with helium, whose two-gasoline power(3hp per engine) are independently radio controlled. The motors and propellers can be tilted and are attached to the gondola through an axle and supporting braces. Four stabilizing fins are mounted at the tail of the airship. To fill in the helium, a valve is placed at the bottom of the hull. The inaugural flight was on jul. 31.2002 at the Pusan, S.korea Most environment monitoring system\ problem use satellite image. But, Low resolution satellite image (multi-spectral) : 1km ∼ 250 m ground resolutions is lows. So, detail information acquisition is hard at the complex terrain. High resolution satellite image (black and white) 30m : The ground resolution is high. But it is high price, visit cycle and delivery time is long So. We want make high accuracy airship photogrammetry system. This airship can catch picture Multi. spectral Aerial photographing (visible, Near infrared and thermal infrared), and High resolution (over 6million pixel). It can take atmosphere datum (Temperature (wet bulb, dew point, general), Pressure (static, dynamic), Humidity, wind speed). this airship is very Quickness that aircraft install time is lower than 30 minutes, it is compact and that conveyance is easy. High-capacity save image (628 cut per 1time (over 6million and 4band(R,G,B,NIR)) and this airship can save datum this High accuracy navigatin (position and rotate angle) by DGPS tech. and Gyro system. this airship will do monitor about red-tide, sea surface temperate, and CH-A, SS and etc.

• Journal of Climate Change Research
• /
• v.2 no.4
• /
• pp.253-267
• /
• 2011

This study summarizes some important results from the studies on clouds and aerosols, and their effects on climate in the northeast Asia that were made mainly by Korean scientists and some other scientists from around the world. Clouds and aerosols are recognized as one of the most important factors that contributes to uncertainties in climate predictions and therefore become the subject of active research in the western developed countries in recent years. However, the researches on clouds and aerosols are very weakly done in Korea except ground based measurements of aerosol physical, chemical and optical properties. These measurements indicate that aerosol loadings in the northeast Asia are generally much higher than other parts of the world. On the other hand, researches on clouds are few in Korea. Satellite and ground remote sensing, numerical modeling and aircraft in-situ measurements of clouds are highly needed for better assessment of the role of clouds on climate in the northeast Asia.

• Journal of Cadastre & Land InformatiX
• /
• v.51 no.1
• /
• pp.125-136
• /
• 2021

Recently, Unmanned Aerial Vehicle utilization and image processing technology for remote sensing have diversified remarkably with Orthophoto and Digital Surface Model. In particular, It uses more application fields such as spatial information analysis and hazardous areas as well as land surveying. This study analyses the accuracy of the coordinate on Orthophoto and DSM height on slope area with high and low differences by using UAV images. As the result of this study, in the case of GCP on 2D orthophoto, the location error was not produced significantly. The vertical position of the DSM showed the highest accuracy when the height difference between GCPs is under 30m(RMSEZ=0.07m). The location of the GCPs was divided into approximately 10m, 20m, 30m, and 40m with analysis for each of the eight points of GCP and inspection points in general. This study expects that producing both horizontal accuracy of Orthophoto and vertical accuracy of DSM using UAV on the sloped area which similar to this research area will help in spatial information fields.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.1
• /
• pp.51-63
• /
• 2022

Up-to-date statistics of crop cultivation status is essential for farm land management planning and the advancement in remote sensing technology allows for rapid update of farming information. The objective of this study was to develop a classification model of rice paddy or winter crop fields based on NDWI, NDVI, and HSV indices using Sentinel-2 satellite images. The 18 locations in central Korea were selected as target areas and photographed once for each during summer and winter with a eBee drone to identify ground truth crop cultivation. The NDWI was used to classify summer paddy fields, while the NDVI and HSV were used and compared in identification of winter crop cultivation areas. The summer paddy field classification with the criteria of -0.195

• Computers and Concrete
• /
• v.33 no.4
• /
• pp.425-443
• /
• 2024

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.1135-1147
• /
• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1739-1756
• /
• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.

• Korean Journal of Remote Sensing
• /
• v.40 no.4
• /
• pp.387-396
• /
• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Korean Journal of Remote Sensing
• /
• v.29 no.5
• /
• pp.545-554
• /
• 2013

Augustine is an active stratovolcano located in southwest of Cook Inlet, about 290 kilometers southwest of Anchorage, Alaska. Between January 11 and 28, 2006, the volcano erupted explosively 14 times. We collected twelve permanent GPS stations operating by Plate Boundary Observatory (PBO) from 2005 to 2011. All data processing was carried out using Bernese GPS Software V5.0 with IGS precise orbit. Static baseline processing by fixing AC59 station was applied for the volcano activity monitoring. AC59 is the nearest (about 24.5 km) station to Augustine volcano, and located on North America Plate including Augustine Island. The test results show inflation (9.7 cm/yr) and deflation (-9.2 cm/yr) of volcano before and after eruption around crater clearly. After volcano activity has reached a plateau, some of the GPS stations installed north of the volcano show ground subsidence phenomenon caused by compaction of pyroclastic flows. These results indicate the possibility of using surface deformation observed by GPS for monitoring and prediction of volcano activity.