• Journal of the Korean Association of Geographic Information Studies
• /
• v.23 no.3
• /
• pp.26-67
• /
• 2020

Synthetic Aperture Radar(SAR) is able to photograph the earth's surface regardless of weather conditions, day and night. Because of its possibility to search for hydrological factors such as soil moisture and groundwater, and its importance is gradually increasing in the field of water resources. SAR began to be mounted on satellites in the 1970s, and about 15 or more satellites were launched as of 2020, which around 10 satellites will be launched within the next 5 years. Recently, various types of SAR technologies such as enhancement of observation width and resolution, multiple polarization and multiple frequencies, and diversification of observation angles were being developed and utilized. In this paper, a brief history of the SAR system, as well as studies for estimating soil moisture and hydrological components were investigated. Up to now hydrological components that can be estimated using SAR satellites include soil moisture, subsurface groundwater discharge, precipitation, snow cover area, leaf area index(LAI), and normalized difference vegetation index(NDVI) and among them, soil moisture is being studied in 17 countries in South Korea, North America, Europe, and India by using the physical model, the IEM(Integral Equation Model) and the artificial intelligence-based ANN(Artificial Neural Network). RADARSAT-1, ENVISAT, ASAR, and ERS-1/2 were the most widely used satellite, but the operation has ended, and utilization of RADARSAT-2, Sentinel-1, and SMAP, which are currently in operation, is gradually increasing. Since Korea is developing a medium-sized satellite for water resources and water disasters equipped with C-band SAR with the goal of launching in 2025, various hydrological components estimation researches using SAR are expected to be active.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.3
• /
• pp.17-24
• /
• 2004

In this paper, the wavelet video coding using Low-Band-Shift(LBS) method and multiresolution motion estimation(MRME) is proposed. To overcome shift- variant property on wavelet coefficients, the LBS was proposed. LBS method previously has superior performance in terms of rate-distortion characteristic. However, this method needs more memory and computational complexity. Therefore to reduce computational complexity of video coding using LBS, we combine MRME with LBS. When mm is applied only, it has 7 times as much as existing method's motion vector because each subband has different motion vector using property of LBS, number of motion vector decreases. Proposed method decreases motion vector, and it decreases motion compensated Prediction error by detailed motion estimation. And then it shows better coding performance. Also this method reduces computational amount by smaller search area in higher resolution. The computational complexity of the proposed method is 12.1% of that of existing method at 3-level wavelet transform. The experimental results with the proposed method show about 0.2∼9.7% improvement of MAD performance in case of lossless coding, and 0.1∼2.0㏈ improvement of PSNR performance at 4he same bit rate in case of lossy coding.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.4
• /
• pp.377-385
• /
• 2015

This paper proposes a novel forward vehicle tracking algorithm based on the WMIL(Weighted Multiple Instance Learning) equipped with a particle filter. In the proposed algorithm Haar-like features are used to train a vehicle object detector to be tracked and the location of the object are obtained from the recognition result. In order to combine both the WMIL to construct the vehicle detector and the particle filter, the proposed algorithm updates the object location by executing the propagation, observation, estimation, and selection processes involved in particle filter instead of finding the credence map in the search area for every frame. The proposed algorithm inevitably increases the computation time because of the particle filter, but the tracking accuracy was highly improved compared to Ababoost, MIL(Multiple Instance Learning) and MIL-based ones so that the position error was 4.5 pixels in average for the videos of national high-way, express high-way, tunnel and urban paved road scene.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.1
• /
• pp.321-340
• /
• 2016

The Compressive Video Sensing (CVS) is a useful technology for wireless systems requiring simple encoders but handling more complex decoders, and its rate-distortion performance is highly affected by the quantization of measurements and reconstruction of video frame, which motivates us to presents the Space-Time Quantization (ST-Q) and Motion-Aligned Reconstruction (MA-R) in this paper to both improve the performance of CVS system. The ST-Q removes the space-time redundancy in the measurement vector to reduce the amount of bits required to encode the video frame, and it also guarantees a low quantization error due to the fact that the high frequency of small values close to zero in the predictive residuals limits the intensity of quantizing noise. The MA-R constructs the Multi-Hypothesis (MH) matrix by selecting the temporal neighbors along the motion trajectory of current to-be-reconstructed block to improve the accuracy of prediction, and besides it reduces the computational complexity of motion estimation by the extraction of static area and 3-D Recursive Search (3DRS). Extensive experiments validate that the significant improvements is achieved by ST-Q in the rate-distortion as compared with the existing quantization methods, and the MA-R improves both the objective and the subjective quality of the reconstructed video frame. Combined with ST-Q and MA-R, the CVS system obtains a significant rate-distortion performance gain when compared with the existing CS-based video codecs.

• Journal of Korea Water Resources Association
• /
• v.56 no.4
• /
• pp.235-243
• /
• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.

• Journal of the military operations research society of Korea
• /
• v.32 no.2
• /
• pp.188-211
• /
• 2006

This study aims to empirically investigate the effects of the individual-level characteristics on their timing decisions for their enlistments even though military services are their duties under a draft system. The individual characteristics considered include five psychological factors, such as attitude, uncertainty, information search level, future expectation, and perceived risk towards army, and other several demographic variables. Measurement scales for these psychological variables are developed and a duration model for individuals' enrollment timing decisions is also proposed. The proposed model is fitted to a survey data set collected from both those who have completed military service and those who have not. The estimation results show that two of five psychological variables, negative attitude and perceived risk, and several demographic variables, including education level, income level, residence area, and the number of family members serving the army, have meaningful impacts on the timing decisions for military service. Specifically, the enlistment timings are found to be more delayed as negative attitude towards army is stronger, perceived risk on army is higher, education level is higher, academic performance is better, income level is either low or high, residence area is either Seoul or big cities, and the proportion of family members enlisted is smaller. Several important managerial implications for alleviating problems resulting from enrollment postponements are also discussed.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1309-1320
• /
• 2023

The technological development and popularization of mobile devices have developed so that users can check their location anywhere and use the Internet. However, in the case of indoors, the Internet can be used smoothly, but the global positioning system (GPS) function is difficult to use. There is an increasing need to provide real-time location information in shaded areas where GPS is not received, such as department stores, museums, conference halls, schools, and tunnels, which are indoor public places. Accordingly, research on the recent indoor positioning technology based on light detection and ranging (LiDAR) equipment is increasing to build a landmark database. Focusing on the accessibility of building a landmark database, this study attempted to develop a technique for estimating the user's location by using a single image taken of a landmark based on a mobile device and the landmark database information constructed in advance. First, a landmark database was constructed. In order to estimate the user's location only with the mobile image photographing the landmark, it is essential to detect the landmark from the mobile image, and to acquire the ground coordinates of the points with fixed characteristics from the detected landmark. In the second step, by applying the bag of words (BoW) image search technology, the landmark photographed by the mobile image among the landmark database was searched up to a similar 4th place. In the third step, one of the four candidate landmarks searched through the scale invariant feature transform (SIFT) feature point extraction technique and Homography random sample consensus(RANSAC) was selected, and at this time, filtering was performed once more based on the number of matching points through threshold setting. In the fourth step, the landmark image was projected onto the mobile image through the Homography matrix between the corresponding landmark and the mobile image to detect the area of the landmark and the corner. Finally, the user's location was estimated through the location estimation technique. As a result of analyzing the performance of the technology, the landmark search performance was measured to be about 86%. As a result of comparing the location estimation result with the user's actual ground coordinate, it was confirmed that it had a horizontal location accuracy of about 0.56 m, and it was confirmed that the user's location could be estimated with a mobile image by constructing a landmark database without separate expensive equipment.

• Journal of Korea Water Resources Association
• /
• v.41 no.2
• /
• pp.149-162
• /
• 2008

The Soil and Water Assessment Tool (SWAT) developed by the USDA-Agricultural Research Service for the prediction of land management impact on water, sediment, and agricultural chemical yields in a large-scale basin was applied to Daecheong Reservoir basin to estimate the amount of soil losses from different land uses. The research outcomes provide important indications for reservoir managers and policy makers to search alternative watershed management practices for the mitigation of reservoir turbidity flow problems. After calibrations of key model parameters, SWAT showed fairly good performance by adequately simulating observed annual runoff components and replicating the monthly flow regimes in the basin. The specific soil losses from agricultural farm field, forest, urban area, and paddy field were 33.1, $2.3{\sim}5.4$ depending on the tree types, 1.0, and 0.1 tons/ha/yr, respectively in 2004. It was noticed that about 55.3% of the total annual soil loss is caused by agricultural activities although agricultural land occupies only 10% in the basin. Although the soil erosion assessment approach adopted in this study has some extent of uncertainties due to the lack of detailed information on crop types and management activities, the results at least imply that soil erosion control practices for the vulnerable agricultural farm lands can be one of the most effective alternatives to reduce the impact of turbidity flow in the river basin system.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.3D
• /
• pp.363-369
• /
• 2008

Change is inevitable and is a reality of construction projects. Change adjustment includes the cost associated with materials, labor, etc. However, the actions of a contractor can cause a loss of productivity and furthermore can result in disruption of the whole project because of a cumulative or ripple effect. Because of its complicated nature, it becomes a complex issue to determine the cumulative impact (ripple effect) caused by single or multiple change orders. Furthermore, owners and contractors do not always agree on the adjusted contract price for the cumulative impact of the changes. What is needed is a reliable method to identify and quantify the loss of productivity caused by cumulative impact of change orders. This study survey the change orders data in domestic area for public construction and analyze to quantify change order impact. This study developed concepts of "%CO", "%Delta", "%T" to capture change order effect on project and search the relationships between them. Finally, this study find strong relationship between change order and loss.