• Korean Journal of Remote Sensing
• /
• v.21 no.4
• /
• pp.341-369
• /
• 2005

Hyperspectral images have emerged as a new and promising remote sensing data that can overcome the limitations of existing optical image data. This study was designed to provide a comprehensive review on definition, data processing methods, and applications of hyperspectral data. Various types of airborne, spaceborne, and field hyperspectral image sensors were surveyed from the available literatures and internet search. To understand the current status of hyperspectral remote sensing technology and research development, we collected several hundreds research papers from international journals (IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing, Remote Sensing of Environment and AVIRIS Workshop Proceedings), and categorized them by sensor types, data processing techniques, and applications. Although several hyperspectral sensors have been developing, AVIRIS has been a primary data source that the most hyperspectral remote sensing researches were relied on. Since hyperspectral data have very large data volume with many spectral bands, several data processing techniques that are particularly oriented to hyperspectral data have been developed. Although atmospheric correction, spectral mixture analysis, and spectral feature extraction are among those processing techniques, they are still in experimental stage and need further refinement until the fully operational adaptation. Geology and mineral exploration were major application in early stage of hyperspectral sensing because of the distinct spectral features of rock and minerals that could be easily observed with hyperspectral data. The applications of hyperspectral sensing have been expanding to vegetation, water resources, and military areas where the multispectral sensing was not very effective to extract necessary information.

• Korean Journal of Remote Sensing
• /
• v.24 no.2
• /
• pp.213-222
• /
• 2008

Although hyperspectral sensing data have shown great potential to derive various surface information that is not usually available from conventional multispectral image, the acquisition of proper hyperspectral image data are often limited. To use ground-based hyperspectral camera image for remote sensing studies, radiometric calibration should be prerequisite. The objective of this study is to develop radiometric calibration procedure to convert image digital number (DN) value to surface reflectance for the 120 bands ground-based hyperspectral camera. Hyperspectral image and spectral measurements were simultaneously obtained from the experimental target that includes 22 different surface materials of diverse spectral characteristics at wavelength range between 400 to 900 nm. Calibration coefficients to convert image DN value to at-sensor radiance were initially derived from the regression equations between the sample image and spectral measurements using ASD spectroradiometer. Assuming that there is no atmospheric effects when the image acquisition and spectral measurements were made at very close distance in ground, we were also able to derive calibration coefficients that directly transform DN value to surface reflectance. However, these coefficients for deriving reflectance values should not be applied when the camera is used for aerial image that contains significant effect from atmosphere and further atmospheric correction procedure is required in such case.

• Korean Journal of Remote Sensing
• /
• v.23 no.6
• /
• pp.537-546
• /
• 2007

In hyperspectral remote sensing, linear spectral mixture model is a common procedure decomposing into the components of a mixed pixel and estimating the fraction of each end-member. Although linear spectral mixture model is frequently used in geology and mineral mapping because this model is simple and easy to apply, this model is not always valid in forest and urban area having rather complex structure. This study aims to analyze possible error for applying linear spectral mixture model. For the study, we measured laboratory spectra of mixture sample, having various materials, fractions, distributions. The accuracy of linear mixture model is low with the mixture sample having similar fraction because the multi-scattering between components is maximum. Additionally, this multi-scattering is related to the types, fraction, and distribution of components. Further analysis is necessary to quantify errors from linear spectral mixture model.

• Korean Journal of Remote Sensing
• /
• v.25 no.1
• /
• pp.71-83
• /
• 2009

This paper is written to analyze possible problems encountered with the existing data for the monitoring of land use and land cover change over the Korean peninsula and, further, to provide technical alternatives for the future land monitoring over the area. The oldest type of non-spatial data related to the land use change are cadastral statistics obtained since 1911. Annual statistics of cadastral data in early years (before 1942) can be used to assess land use change over the area. However, the cadastral statistics after the Korean War are not very appropriate for land use monitoring since the land class in cadastral data does not always correspond with actual land cover status. Majority of spatial data available for land monitoring over the area are land cover maps classified from satellite imagery since early 1970's. To analyze the suitability of land cover maps that were produced by two separate institutes with about 10 years interval, we conducted simple change detection analysis using these maps. These maps were not quite ready to be compared each other, in which they did not have the same class definition, classification method, and geometric registration. To achieve continuous and effective monitoring of land use and land cover change, particularly over North Korea, we should have a standard scheme in type and season of satellite imagery, image classification procedure, and class definition, which also should correspond to international standards.

• Korean Journal of Remote Sensing
• /
• v.22 no.6
• /
• pp.565-574
• /
• 2006

Satellite images have been used to obtain land cover information that is one of important factors for hydrological analysis over a large area. In urban area, more detailed land cover data are often required for hydrological analysis because of the relatively complex land cover types. The number of land cover classes that can be classified with traditional multispectral data is usually less than the ones required by most hydrological uses. In this study, we present the capabilities of hyperspectral data (Hyperion) for the classification of hydrological land cover types in urban area. To obtain 17 classes of urban land cover defined by the USDA SCS, spectral mixture analysis was applied using eight endmembers representing both impervious and pervious surfaces. Fractional values from the spectral mixture analysis were then reclassified into 17 cover types according to the ratio of impervious and pervious materials. The classification accuracy was then assessed by aerial photo interpretation over 10 sample plots.

• Food Science and Preservation
• /
• v.19 no.6
• /
• pp.873-881
• /
• 2012

This study was to examine the quality characteristics of Cedrela shoots according to soy sauce conditions. First, the results were on solid samples (pickled Cedrela sinensis shoot) as follows: The pH and acidity of Cedrela shoots tended to decrease slightly in the licorice added section. The sugar content and salinity of Cedrela shoots tended to decrease gradually during the storage period. The color values of Cedrela shoots tended to change from green to dark green due to soy sauce as their storage periods. The texture of Cedrela shoots had differences according to their unique characteristics, but the change was small in the licorice added section. The overall preference appeared most high in P1-1. Next, the results were on Liquid samples (soy sauce) as follows: The pH and acidity of soy sauce tended to decrease gradually during the storage period. The sugar content and salinity did not change a lot. Microorganisms were not detected during the storage period in the established conditions. Therefore, the conditions for quality changes about pickling soy sauce during the storage period have been established and it can be anticipated to commercialize various pickle food using Cedrela shoots.

• Food Science and Preservation
• /
• v.19 no.4
• /
• pp.501-509
• /
• 2012

The quality characteristics of Acanthopanax and Cedrela shoots during their preservation were investigated according to the salting conditions to improve the use of the shoots. The results were as follows: The pH of added ionization mineral during the storage did not change. The salinity of the salt-stored Acanthopanax and Cedrela tended to insignificantly change as their storage temperature changed and increase during the addition of the ionization mineral. In terms of the color values, the b value of the added non-ionization mineral tended to increase and that of the added ionization mineral tended to gradually decrease. The texture of Acanthopanax and Cedrela shoots to which an ionization mineral was added tended to decrease gradually during their storage, and was highest when 50 ppm of the ionization mineral was added. The total chlorophyll contents tended to decrease during the salt storage, did not change significantly with the change in the added ionization mineral, and decreased at the storage temperature of $10^{\circ}C$. Therefore, it can be concluded that quality of Acanthopanax and Cedrela shoots can be maintained when they are stored in ionization mineral addition.

• Journal of Soil and Groundwater Environment
• /
• v.19 no.4
• /
• pp.62-69
• /
• 2014

In the results of monitoring nitrate concentration in more than 8,000 groundwater wells around agro-livestock, the average and maximum nitrate concentration was 9.4 mg/L and 101.2 mg/L, respectively. Since about 31% of the monitoring wells was exceed the quality standard for drinking water, nitrate control such as remediation or source regulation is required to conserve safe-groundwater in South Korea. Typical nitrate-treatment technologies include ion exchange, reverse osmosis, and biological denitrification. Among the treatment methods, biological denitrification by indigenous microorganism has environmental and economic advantages for the complete elimination of nitrate because of lower operating costs compared to other methods. Major mechanism of the process is microbial reduction of nitrate to nitrite and nitrogen gas. Three functional genes (nosZ, nirK, nirS) that encode for the enzyme involved in the pathway. In this work, we tried to develop simple process to determine possibility of natural denitrification reaction by monitoring the functional gene. For the work, the functional genes in nitrate-contaminated groundwater were monitored by using PCR with specific target primers. In the result, functional genes (nosZ and nirK) encoding denitrification enzymes were detected in the groundwater samples. This method can help to determine the possibility of natural-nitrate degradation in target groundwater wells without multiplex experimental process. In addition, for field-remediation application we selected nitrate-contaminated site where 200~600 mg/L of nitrate is continuously detected. To determine the possibility of nitrate-degradation by stimulated-natural attenuation, groundwater was sampled in two different wells of the site and nitrate concentration of the samples was 300 mg/L and 616 mg/L, respectively. Fumarate for different C/N ratio was added into microcosm bottles containing the groundwater to examine denitrification rate depending on carbon concentration. In the result, once 1.5 times more than amount of fumarate stoichiometry required was added, the 616 mg/L of nitrate and 300 mg/L of nitrate were completely degraded in 8 days and 30 days. The nitrite, byproduct of denitrification process, was also completely degraded during the experimental period.

• Journal of Korean Clinical Nursing Research
• /
• v.17 no.1
• /
• pp.113-122
• /
• 2011

Purpose: This study was conducted to develop and evaluate an oral care protocol for cancer patients receiving chemotherapy. Methods: The participants in this study were cancer patients in Severance hospital in Seoul. A total of 31 patients were assigned to the experimental group, and 29 patients to the control group. Data were collected from August 1 to October 5, 2010. The protocol included definition and symptom of oral mucositis, self-check method of oral status, prophylactic method of oral mucositis (oral care, eating habits, and gargling) and management of oral mucositis. Oral Assessment Guide (OAG) was used as the measurement tool of oral mucositis in this study, Oral Care Performance Scale was used as the measurement tool of oral care performance status. Data were analyzed with a $x^2-test$ and t-test, and repeated measures ANOVA, using SPSS/WIN 18.0 program. Results: Patients in the experimental group receiving the oral care protocol had a significant difference (t=-2.938, p=.005) in the oral care performance compared to the control group. However, there was no significant difference (F=1.255, p=.274) in the oral mucositis status between two groups. Conclusion: This study showed that the oral care protocol could improve oral care performance status for the patients under chemotherapy.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.11
• /
• pp.457-464
• /
• 2019

The weight of pig is one of the main factors in determining the health and growth state of pigs, their shipment, the breeding environment, and the ration of feed, and thus measuring the pig's weight is an important issue in productivity perspective. In order to estimate the pig's weight by using the number of pig's pixels from images, acquired from a Top-view camera, the posture determining and the head removal from images are necessary to measure the accurate number of pixels. In this research, we propose the fast and accurate method to determine the pig's posture by using a fast image processing technique, find the head location by using a fast deep learning technique, and remove pig's head by using light weighted image processing technique. First, we determine the pig's posture by comparing the length from the center of the pig's body to the outline of the pig in the binary image. Then, we train the location of pig's head, body, and hip in images using YOLO(one of the fast deep learning based object detector), and then we obtain the location of pig's head and remove an outside area of head by using head location. Finally, we find the boundary of head and body by using Convex-hull, and we remove pig's head. In the Experiment result, we confirmed that the pig's posture was determined with an accuracy of 0.98 and a processing speed of 250.00fps, and the pig's head was removed with an accuracy of 0.96 and a processing speed of 48.97fps.