• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 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 Soil Science and Fertilizer
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• v.42 no.6
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• pp.522-528
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• 2009

This study focused on establishing prediction models using visible-near infrared spectrum to simultaneously detect multiple components of soils and enhancing the performance quality by suitably transformed input spectra and classification of soil spectral types for prediction model input. The continuum-removed spectra showed significant result for all cases in terms of soil properties and classified or bulk predictions. The prediction model using classified soil spectra at an absorption peak area around 500nm and 950nm efficiently indicating soil color showed slightly better performance. Especially, Ca and CEC were well estimated by the classified prediction model at $R^{2}$ > 0.8. For organic carbon, both classified and bulk prediction model had a good performance with $R^{2}$ > 0.8 and RPD> 2. This prediction model may be applied in global soil mapping, soil classification, and remote sensing data analysis.

• Asian Journal of Turfgrass Science
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• v.22 no.1
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• pp.1-12
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• 2008

Remote sensing using multispectral radiometry may be a useful tool to detect drought stress in turf. The objective of this research was to investigate the correlation between drought stress and multispectral reflectance (MSR) from the turf canopy. St. Augustinegrass (Stenotaphrum secundatum[Walt.] Kuntze.) cultivars 'Floratam' and 'Palmetto', 'SeaIsle 1' seashore paspalum Paspalum vaginatum Swartz.), 'Empire' zoysiagrass (Zoysia japonica Steud.), and 'Pensacola' bahiagrass (Paspalum notatumFlugge) were established in lysimeters in the University of Florida Envirotron greenhouse facility in Gainesville. Irrigation was applied at 100%, 80%, 60%, or 40% of evapotranspiration (ET). Weekly evaluations included: a) shoot quality, leaf rolling, leaf firing b) soil moisture, chlorophyll content index; c) photosynthesis and d) multispectral reflectance. All the measurements were correlated with MSR data. Drought stress affected the infrared spectral region more than the visible spectral region. Reflectance sensitivity to water content of leaves was higher in the infrared spectral region than in the visible spectral region. Grasses irrigated at 100% and 80% of ET had no differences in normalized difference vegetation indices (NDVI), leaf area index (LAI), and stress indices. Grasses irrigated at 60% and 40% of ET had differences in NDVI, LAI, and stress indices. All measured wavelengths except 710nm were highly correlated (P < 0.0001) with turf visual quality, leaf firing, leaf rolling, soil moisture, chlorophyll content index, and photosynthesis. MSR could detect drought stress from the turf canopy.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.7
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• pp.433-441
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• 2007

The bio-Sensors, which are sensing the vital signs of human bodies, are largely used by the medical equipment. Recently, the sensor network technology, which composes of the sensor interface for small-seize hardware, processor, the wireless communication module and battery in small sized hardware, has been extended to the area of bio-senor network systems due to the advances of the MEMS technology. In this paper we have suggested a design and implementation of a health care information system(called u-EMS) using a bio-sensor network technology that is a combination of the bio-sensor and the sensor network technology. In proposed system, we have used the following vital body sensors such as EKG sensor, the blood pressure sensor, the heart rate sensor, the pulse oximeter sensor and the glucose sensor. We have collected various vital sign data through the sensor network module and processed the data to implement a health care measurement system. Such measured data can be displayed by the wireless terminal(PDA, Cell phone) and the digital-frame display device. Finally, we have conducted a series of tests which considered both patient's vital sign and context-awared information in order to improve the effectiveness of the u-EMS.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.333-340
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• 2011

The Human-Computer Interaction(HCI) is a study of the method for interaction between human and computers that merges the ergonomics and the information technology. The intelligent space, which is a part of the HCI, is an important area to provide effective user interface for the disabled, who are alienated from the information-oriented society. In the intelligent space for the disabled, the method supporting information depends on types of disability. In this paper, we only support the hearing-impaired. It is material to the gaze direction detection method because it is very efficient information provide method to present information on gazing direction point, except for the information provide location perception method through directly contact with the hearing-impaired. We proposed the gaze direction detection method must be necessary in order to provide the residence life application to the hearing-impaired like this. The proposed method detects the region of the user from multi-view camera images, generates candidates for directions of gaze for horizontal and vertical from each camera, and calculates the gaze direction of the user through the comparison with the size of each candidate. In experimental results, the proposed method showed high detection rate with gaze direction and foot sensing rate with user's position, and showed the performance possibility of the scenario for the disabled.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.120-131
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• 2020

Water body detection using remote sensing based on machine interpretation of satellite image is efficient for managing water resource, drought and flood monitoring. In this study, water body detection with SAR satellite image based on machine learning was performed. However, non water body area can be misclassified to water body because of shadow effect or objects that have similar scattering characteristic comparing to water body, such as roads. To decrease misclassifying, 8 combination of morphology open filtered band, DEM band, curvature band and Cosmo-SkyMed SAR satellite image band about Mokpo region were trained to semantic segmentation machine learning models, respectively. For 8 case of machine learning models, global accuracy that is final test result was computed. Furthermore, concordance rate between landcover data of Mokpo region was calculated. In conclusion, combination of SAR satellite image, morphology open filtered band, DEM band and curvature band showed best result in global accuracy and concordance rate with landcover data. In that case, global accuracy was 95.07% and concordance rate with landcover data was 89.93%.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.104-124
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• 2017

This study aimed to validate the ASTER surface temperature using field measurements over various land use types in the urban area of Changwon City, South Korea. The ASTER surface temperature was measured by collecting eight images during daytime and nighttime in June and September from 2012 to 2014, and field measurements were conducted over the same period when the satellite images were taken. The analyses showed that the surface temperature measured in the field during the daytime was higher than that of satellite imageries by $5{\sim}10^{\circ}C$, and the gap was higher in built-up areas. The calibration models of surface temperature showed a 60% explanatory power in areas other than parks, indicating that the models are reliable. During nighttime, except for the summer month of August, ASTER surface temperature was determined to be approximately $2^{\circ}C$ higher in contrast to daytime.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.499-506
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• 2019

NDSM (Normalized Digital Surface Model) is key information for the detailed analysis of remote sensing data. Although NDSM can be simply obtained by subtracting a DTM (Digital Terrain Model) from a DSM (Digital Surface Model), in case of UAV (Unmanned Aerial Vehicle) data, it is difficult to get an accurate DTM due to high resolution characteristics of UAV data containing a large number of complex objects on the ground such as vegetation and urban structures. In this study, RGB-based UAV vegetation index, ExG (Excess Green) was used to extract initial seed points having low ExG values for region growing such that a DTM can be generated cost-effectively based on high resolution UAV data. For this process, local window analysis was applied to resolve the problem of erroneous seed point extraction from local low ExG points. Using the DSM values of seed points, region growing was applied to merge neighboring terrain pixels. Slope criteria were adopted for the region growing process and the seed points were determined as terrain points in case the size of segments is larger than 0.25 ㎡. Various slope criteria were tested to derive the optimized value for UAV data-based NDSM generation. Finally, the extracted terrain points were evaluated and interpolation was performed using the terrain points to generate an NDSM. The proposed method was applied to agricultural area in order to extract the above ground heights of crops and check feasibility of agricultural monitoring.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.563-570
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• 2019

Red tide has caused massive fish kills in Korean coastal waters with devastating economic loss in the aquaculture industry since 1995. Remote sensing technique has shown to be effective for the detection of red tide in wide areas, where the absorption property of red tide water plays a central role in understanding the red tide reflectance. This study analyzed the optical absorption property of sea waters dominated by the dinoflagellate specie of Alexandirum affine, off the Tongyeong area in August, 2017. Water samples collected from 20 stations in the ship-based campaign were measured for absorption by pigment, suspended solid, and dissolved organic matter, with the corresponding water quality variables such as chlorophyll concentration and total suspended solid. The analysis showed that Alexandrium-dominated water exhibits strong absorption in the spectral range below 400 nm unlike that of diatom-dominated waters, and greater fluctuations in the range of 400 nm - 500 nm. The packaging effect in pigment absorption was stronger in Alexandrium-dominated waters, and the exponent in the absorption by detritus and gelbstoff is disparate for diatom and Alexandrium. In the model for the detritus and gelbstoff absorption (a_dg(λ)=a_dg(λ₀)e^-s(λ-λ₀)), the optimal exponent coefficient(s) for the Alexandrium was close to 0.01 rather than to 0.015, which was commonly use for modelling diatom waters.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.663-666
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• 2005

Gas hydrates are ice-like compounds that form at the low temperature and high pressure conditions common in shallow marine sediments at water depths greater than 300-500 m when concentrations of methane and other hydrocarbon gases exceed saturation. Estimates of the total mass of methane carbon that resides in this reservoir vary widely. While there is general agreement that gas hydrate is a significant component of the global near-surface carbon budget, there is considerable controversy about whether it has the potential to be a major source of fossil fuel in the future and whether periods of global climate change in the past can be attributed to destabilization of this reservoir. Also essentially unknown is the interaction between gas hydrate and the subsurface biosphere. ODP Leg 204 was designed to address these questions by determining the distribution, amount and rate of formation of gas hydrate within an accretionary ridge and adjacent basin and the sources of gas for forming hydrate. Additional objectives included identification of geologic proxies for past gas hydrate occurrence and calibration of remote sensing techniques to quantify the in situ amount of gas hydrate that can be used to improve estimates where no boreholes exist. Leg 204 also provided an opportunity to test several new techniques for sampling, preserving and measuring gas hydrates. During ODP Leg 204, nine sites were drilled and cored on southern Hydrate Ridge, a topographic high in the accretionary complex of the Cascadia subduction zone, located approximately 80km west of Newport, Oregon. Previous studies of southern Hydrate Ridge had documented the presence of seafloor gas vents, outcrops of massive gas hydrate, and a pinnacle' of authigenic carbonate near the summit. Deep-towed sidescan data show an approximately $300\times500m$ area of relatively high acoustic backscatter that indicates the extent of seafloor venting. Elsewhere on southern Hydrate Ridge, the seafloor is covered with low reflectivity sediment, but the presence of a regional bottom-simulating seismic reflection (BSR) suggests that gas hydrate is widespread. The sites that were drilled and cored during ODP Leg 204 can be grouped into three end-member environments basedon the seismic data. Sites 1244 through 1247 characterize the flanks of southern Hydrate Ridge. Sites 1248-1250 characterize the summit in the region of active seafloor venting. Sites 1251 and 1252 characterize the slope basin east of Hydrate Ridge, which is a region of rapid sedimentation, in contrast to the erosional environment of Hydrate Ridge. Site 1252 was located on the flank of a secondary anticline and is the only site where no BSR is observed.