• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.523-535
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• 2011

The precipitable water vapor (PW) was estimated using Global Navigation Satellite System (GNSS) from several GNSS stations within the Korean Peninsula. Nearby radiosonde sites covering the GNSS stations were used for the comparison and validation of test results. GNSS data recorded under typical and severe weather conditions were used to generalize our approach. Based on the analysis, we have confirmed that the derived PW values from the GNSS observables were well agreed on the estimates from the radiosonde observables within 10 mm level. Assuming that the GNSS observables could be a good weather monitoring tool, we further tested the performance of the current WAAS tropospheric delay model, UNB3, in the Korean Peninsula. Especially, the wet zenith delays estimated from the GNSS observables and from UNB3 delay model were compared. Test results showed that the modelled approach for the troposphere (i.e., UNB3) did not perform well especially under the wet weather conditions in the Korean Peninsula. It was suggested that a new model or a near real-time model (e.g., based on regional model from GNSS or numerical weather model) would be highly desirable for the Korean WA-DGNSS to minimize the effects of the tropospheric delay and hence to achieve high precision vertical navigation solutions.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.3
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• pp.69-81
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• 2009

This study was intended to evaluate the objective landscape of rural region using an additive integration index method in the Seondong region of Gochang-gun, Jeollabuk-do, Korea. This study consisted of the following three steps. First, this study developed an additive integration index calculation model for landscape assessment based on indicators and weight to each space type in accordance with three landscape fields which were developed by the expert Delphi method. Second, this study used NDVI (Normalized Difference Vegetation Index) and permeable area rate, which were available from high resolution satellite image, to calculate the green naturality degree, area rate, and building coverage respectively. Third, this study has calculated the landscape assessment index of rural regions using an additive integration index method made of assessment data and weight for each indicator. This study has found the following results: 1) landscape level was very poor in all 6 types of space, marking grade five; 2) while the highest level of natural landscape and mixed landscape was grade two, that of artificial landscape was grade five; 3) based on objective landscape, grade five showed the highest frequency, and grade one, two, three, and four followed in that order.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.3-12
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• 2010

In previous studies, the digital measurement systems and analysis algorithms were developed by using the related techniques, such as the aerial photograph detection and high resolution satellite image process. However, these studies were limited in 2-dimensional geo-processing. Therefore, it is necessary to apply the 3-dimensional spatial information and coordinate system for higher accuracy in recognizing and locating of geo-features. The objective of this study was to develop a stochastic algorithm for the optimal sensor placement using the 3-dimensional spatial analysis method. The 3-dimensional information of the LiDAR was applied in the sensor field algorithm based on 2- and/or 3-dimensional gridded points. This study was conducted with three case studies using the optimal sensor placement algorithms; the first case was based on 2-dimensional space without obstacles(2D-non obstacles), the second case was based on 2-dimensional space with obstacles(2D-obstacles), and lastly, the third case was based on 3-dimensional space with obstacles(3D-obstacles). Finally, this study suggested the methodology for the optimal sensor placement - especially, for ground-settled sensors - using the LiDAR data, and it showed the possibility of algorithm application in the information collection using sensors.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.143-154
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• 2004

The researchers, who seek geological and environmental information, depend on the remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, the adverse weather conditions and the expensive equipment can restrict that the researcher can collect their data anywhere and any time. To allow for better flexibility, we have developed a compact, a multi-spectral automatic Aerial photographic system(PKNU 2). This system's Multi-spectral camera can catch the visible(RGB) and infrared(NIR) bands($3032{\times}2008$ pixels) image. Visible and infrared bands images were obtained from each camera respectively and produced Color-infrared composite images to be analyzed in the purpose of the environment monitor but that was not very good data. Moreover, it has a demerit that the stereoscopic overlap area is not satisfied with 60% due to the 12s storage time of each data, while it was possible that PKNU 2 system photographed photos of great capacity. Therefore, we have been developing the advanced PKNU 2(PKNU 3) that consists of color-infrared spectral camera can photograph the visible and near infrared bands data using one sensor at once, thermal infrared camera, two of 40 G computers to store images, and MPEG board to compress and transfer data to the computer at the real time and can attach and detach itself to a helicopter. Verification and calibration of each sensor(REDLAKE MS 4000, Raytheon IRPro) were conducted before we took the aerial photographs for obtaining more valuable data. Corrections for the spectral characteristics and radial lens distortions of sensor were carried out.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.724-729
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• 2008

In the case of 'MAEMI', the Typhoon which formed in September, 2003, the largest-scale damage of tidal wave was caused by the co-occurrence of Typhoon surge and full tide. Until now Korea has been focusing on the calculating the amount of damage and its restoration to cope with these sea and harbor disasters. It is essential to establish some systematic counterplans to diminish such damages of large-scale tidal invasion on coastal lowlands considering the recent weather conditions of growing scale of typhoons. Therefore, the purpose of this research is to make the counterplans for prevention against disasters fulfilled effectively based on the data conducted by comparing and analyzing the accuracy between observation values and the results of estimating the greatest overflow area according to abnormal tidal levels centered on Masan area where there was the severest damage from tidal wave at that time. It's necessary utilize data like high-resolution satellite image and LiDAR(etc.) for correct analysis data considering geographical characteristics of dangerous area from the storm surge. And we must make a solution to minimize the damage by making data of dangerous section of flood into GIS Database using those data (as stated above) and drawing correcter damage function.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.847-859
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• 2021

Recently, as the number of high-resolution satellite SAR images increases, the demand for precise matching of SAR imagesin change detection and image fusion is consistently increasing. RMSE (Root Mean Square Error) values using GCPs (Ground Control Points) selected by analysts have been widely used for quantitative evaluation of image registration results, while it is difficult to find an approach for automatically measuring the registration accuracy. In this study, a feasibility analysis was conducted on using the FSIM (Feature Similarity) index as a measure to evaluate the registration accuracy. TerraSAR-X (TSX) staring spotlight data collected from various incidence angles and orbit directions were used for the analysis. FSIM was almost independent on the spatial resolution of the SAR image. Using a single SAR image, the FSIM with respect to registration errors was analyzed, then use it to compare with the value estimated from TSX data with different imaging geometry. FSIM index slightly decreased due to the differencesin imaging geometry such as different look angles, different orbit tracks. As the result of analyzing the FSIM value by land cover type, the change in the FSIM index according to the co-registration error was most evident in the urban area. Therefore, the FSIM index calculated in the urban was mostsuitable for determining the accuracy of image registration. It islikely that the FSIM index has sufficient potential to be used as an index for the co-registration accuracy of SAR image.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.78-90
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• 2021

In order to construct a precision geoid, it has been diversified into land, sea, aviation, and satellite gravity measurement methods, and measurement technology has developed, making it possible to secure high-resolution, high-precision gravity data. The construction of precision geoids can be fast and conveniently decided through GNSS surveys without separate leveling, and since 2014, the National Geographic Information Institute has been developing a hybrid geoid model to improve the accuracy of height surveying based on GNSS. In this study, the results of the GNSS height measurement were compared and analyzed choosing existing public reference points to verify the GNSS height measurement of public surveys. Experiments are conducted with GNSS height measurements and analyzed precision for public reference points on coastal, border, and mountainous terrain presented as low-precision areas or expected-to-be low-precision in research reports. To verify the GNSS height measurement, the GNSS ellipsoid height of the surrounding integrated datum to be used as a base point for the GNSS height measurement at the public datum. Based on the checked integrated datum, the GNSS ellipsoid of the public datum was calculated, and the elevation was calculated using the KNGeoid18 model and compared with the results of the direct level measurement elevation. The analysis showed that the results of GNSS height measurement at public reference points in the coastal, border, and mountainous areas were satisfied with the accuracy of public level measurement in grades 3 and 4. Through this study, GNSS level measurement can be used more efficiently than existing direct level measurements depending on the height accuracy required by users, and KNGeoids 18 can also be used in various fields such as autonomous vehicles and unmanned aerial vehicles.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1015-1023
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• 2022

The Entry-Descent-Landing process of a lander involves many environmental and technical challenges. To solve these problems, recently, terrestrial relative navigation (TRN) technology has been essential for landers. TRN is a technology for estimating the position and attitude of a lander by comparing Inertial Measurement Unit (IMU) data and image data collected from a descending lander with pre-built reference data. In this paper, we present a method for generating descent dataset and extracting landmarks, which are key elements for developing TRN technologies to be used on Mars. The proposed method generates IMU data of a descending lander using a simulated Mars landing trajectory and generates descent images from high-resolution ortho-map and digital elevation map through a ray tracing technique. Landmark extraction is performed by an area-based extraction method due to the low-textured surfaces on Mars. In addition, search area reduction is carried out to improve matching accuracy and speed. The performance evaluation result for the descent dataset generation method showed that the proposed method can generate images that satisfy the imaging geometry. The performance evaluation result for the landmark extraction method showed that the proposed method ensures several meters of positioning accuracy while ensuring processing speed as fast as the feature-based methods.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1925-1934
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• 2022

Monitoring nuclear activity for inaccessible areas using remote sensing technology is essential for nuclear non-proliferation. In recent years, deep learning has been actively used to detect nuclear-activity-related small objects. However, high-resolution satellite imagery containing small objects can result in class imbalance. As a result, there is a performance degradation problem in detecting small objects. Therefore, this study aims to improve detection accuracy by analyzing the effect of the ratio of small objects related to nuclear activity in the input data for the performance of the deep learning model. To this end, six case datasets with different ratios of small object pixels were generated and a U-Net model was trained for each case. Following that, each trained model was evaluated quantitatively and qualitatively using a test dataset containing various types of small object classes. The results of this study confirm that when the ratio of object pixels in the input image is adjusted, small objects related to nuclear activity can be detected efficiently. This study suggests that the performance of deep learning can be improved by adjusting the object pixel ratio of input data in the training dataset.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.597-608
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• 2022

Remotely sensed data have been used in various fields, such as disasters, agriculture, urban planning, and the military. Recently, the demand for the multitemporal dataset with the high-spatial-resolution has increased. This manuscript proposed an automatic image matching algorithm using a deep learning technique to utilize a multitemporal remotely sensed dataset. The proposed deep learning model was based on High Resolution Net (HRNet), widely used in image segmentation. In this manuscript, denseblock was added to calculate the correlation map between images effectively and to increase learning efficiency. The training of the proposed model was performed using the multitemporal orthophotos of the National Geographic Information Institute (NGII). In order to evaluate the performance of image matching using a deep learning model, a comparative evaluation was performed. As a result of the experiment, the average horizontal error of the proposed algorithm based on 80% of the image matching rate was 3 pixels. At the same time, that of the Zero Normalized Cross-Correlation (ZNCC) was 25 pixels. In particular, it was confirmed that the proposed method is effective even in mountainous and farmland areas where the image changes according to vegetation growth. Therefore, it is expected that the proposed deep learning algorithm can perform relative image registration and image matching of a multitemporal remote sensed dataset.