• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.89-98
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• 2009

In the field of computer vision, image mosaicking is a common method for effectively increasing restricted the field of view of a camera by combining a set of separate images into a single seamless image. Image mosaicking based on feature points has recently been a focus of research because of simple estimation for geometric transformation regardless distortions and differences of intensity generating by motion of a camera in consecutive images. Yet, since most feature-point matching algorithms extract feature points using gray values, identifying corresponding points becomes difficult in the case of changing illumination and images with a similar intensity. Accordingly, to solve these problems, this paper proposes a method of image mosaicking based on feature points using color information of images. Essentially, the digital values acquired from a digital color camera are converted to values of a virtual camera with distinct narrow bands. Values based on the surface reflectance and invariant to the chromaticity of various illuminations are then derived from the virtual camera values and defined as color-invariant values invariant to changing illuminations. The validity of these color-invariant values is verified in a test using a Macbeth Color-Checker under simulated illuminations. The test also compares the proposed method using the color-invariant values with the conventional SIFT algorithm. The accuracy of the matching between the feature points extracted using the proposed method is increased, while image mosaicking using color information is also achieved.

• Journal of the Korean Solar Energy Society
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• v.35 no.2
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• pp.73-83
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• 2015

When it comes to these buildings for business use, cooling load during summertime was reported to have great importance which, as a result, impressively increased interest in Solar Heat Gain Coefficient (SHGC). Such SHGC is considered to be lowered with the help of colors and functions of glass itself, internal shading devices, insulation films and others but basically, these external shading devices for initial blocking that would not allow solar heat to come in from outside the buildings are determined to be most effective. Of many different external shading devices, this thesis conducted an analysis on Exterior Venetian Blind. As for vertical shading devices, previous researches already calculated SHGC conveniently using concepts of sky-opening ratios. However in terms of the Venetian Blind, such correlation is not possibly applied. In light of that, in order to extract a valid correlation, this study first introduced a concept called shape factor, which would use the breadth and a space of a shade, before carrying out the analysis. As a consequence, the concept helped this study to find a very similar correlation. Results of the analysis are summarized as follows. (1) Regarding SHGC depending on the surface reflectance of a shade, an average of 2% error is observed and yet, the figure can always be ignored when it comes to a simple calculation. (2) As for SHGC of each bearing, this study noticed deviations of 4% or less and in the end, it is confirmed that extraction can be achieved with no more than one correlation formula. (3) When only the shape factor and nothing else is used for finding a correlation formula, the formula with a deviation of approximately 5% or less is what one would expect. (4) Since the study observed slight differences in bearings depending on ranges of the shape factors, it needed to extract a weighted value of each bearing, and learned that the smaller the shape factor, the wider the range of a weighted value. The study now suggests that a follow-up research to extract a simple calculation formula by dealing with all these various inclined angles of shade, solar radiation conditions of each region (the ratio of diffuse radiation to direct radiation and others) as well as seasonal features should be carried out.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.48-63
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• 2015

In this study, we introduce various hyperspectral data processing techniques for the monitoring of shallow and coastal waters to enlarge the application range and to improve the accuracy of the end results in Korea. Unlike land, more accurate atmospheric correction is needed in coastal region showing relatively low reflectance in visible wavelengths. Sun-glint which occurs due to a geometry of sun-sea surface-sensor is another issue for the data processing in the ocean application of hyperspectal imagery. After the preprocessing of the hyperspectral data, a semi-analytical algorithm based on a radiative transfer model and a spectral library can be used for bathymetry mapping in coastal area, type classification and status monitoring of benthos or substrate classification. In general, semi-analytical algorithms using spectral information obtained from hyperspectral imagey shows higher accuracy than an empirical method using multispectral data. The water depth and quality are constraint factors in the ocean application of optical data. Although a radiative transfer model suggests the theoretical limit of about 25m in depth for bathymetry and bottom classification, hyperspectral data have been used practically at depths of up to 10 m in shallow and coastal waters. It means we have to focus on the maximum depth of water and water quality conditions that affect the coastal applicability of hyperspectral data, and to define the spectral library of coastal waters to classify the types of benthos and substrates.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.409-418
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• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1935-1943
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• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1513-1513
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• 2001

Present Food Legislation compels dairy industry to carry out analyses in order to guarantee the food safety and quality of products. Furthermore, in many cases industry pays milk according to bacteriological or/and nutritional quality. In order to do these analyses, several expensive instruments are needed (Milkoscan, Fossomatic, Bactoscan). NIRS technology Provides a unique instrument to deal with all analytical requirements. It offers as main advantages its speed and, specially, its versatility, since not only allows determine all the parameters required in milk analysis, but also allows analyse other dairy products, like cheese or whey. The objective of this study is to develop NIRS calibration equations to predict several quality parameters in goat milk, cheese and whey. Three sets of 123 milk samples, 190 cheese samples and 109 whey samples, have been analysed in a FOSS NIR Systems 6500 I spectrophotometer equipped with a spinning module. Milk and whey were analysed by folded transmission, using circular cells with gold surface and pathlength of 0.1 m, while intact cheese was analysed by reflectance using standard circular cells. NIRS calibrations were obtained for the prediction of chemical composition in goat milk, for fat (r$^2$=0.92; SECV=0.20%), total solids (r$^2$=0.95: SECV=0.22%), protein (r$^2$=0.94; SECV=0.07%), casein (r$^2$=0.93; SECV=0.07%) and lactose (r$^2$=0.89; SECV=0.05%). Moreover, equations have been performed to determine somatic cells (r$^2$=0.81; SECV=276.89%) and total bacteria (r$^2$=0.58; SECV=499.32%) counts in goat milk. In the case of cheese, calibrations were obtained for the prediction of fat (r$^2$=0.92; SECV=0.57), total solids (r$^2$=0.80; SECV=0.92%) and protein (r$^2$=0.70; SECV=0.63%). In whey, fat (r$^2$=0.66; SECV=0.08%), total solids (r$^2$=0.67; SECV=0.19%) and protein (r$^2$=0.76; SECV=0.07%) NIRS equations were obtained. These results proved the viability of NIRS technology to predict chemical and microbiological parameters and somatic cells count in goat milk, as well as chemical composition of goat cheese and whey.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1185-1193
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• 2023

The increasing interest in soil moisture data from satellite imagery for applications in hydrology, meteorology, and agriculture has led to the development of methods to produce variable-resolution soil moisture maps. Research on accurate soil moisture estimation using satellite imagery is essential for remote sensing applications. The purpose of this study is to generate a soil moisture estimation map for a test area using KOMPSAT-3/3A and KOMPSAT-5 SAR imagery and to quantitatively compare the results with soil moisture data from the Soil Moisture Active Passive (SMAP) mission provided by NASA, with a focus on accuracy validation. In addition, the Korean Environmental Geographic Information Service (EGIS) land cover map was used to determine soil moisture, especially in agricultural and forested regions. The selected test area for this study is the western part of Jeju, South Korea, where input data were available for the soil moisture estimation algorithm based on the Water Cloud Model (WCM). Synthetic Aperture Radar (SAR) imagery from KOMPSAT-5 HV and Sentinel-1 VV were used for soil moisture estimation, while vegetation indices were calculated from the surface reflectance of KOMPSAT-3 imagery. Comparison of the derived soil moisture results with SMAP (L-3) and SMAP (L-4) data by differencing showed a mean difference of 4.13±3.60 p% and 14.24±2.10 p%, respectively, indicating a level of agreement. This research suggests the potential for producing highly accurate and precise soil moisture maps using future South Korean satellite imagery and publicly available data sources, as demonstrated in this study.

• Journal of the Korea Organic Resources Recycling Association
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• v.32 no.1
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• pp.39-47
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• 2024

In this study, LiDAR-detective black material is synthesized by recycling silicon sludge (SS) that is generated from semiconductor manufacturing process, and its recognition is confirmed using two types of LiDAR sensors (MEMS and Rotating LiDAR). In detail, metal impurities on the surface of SS is removed, followed by coating of titanium dioxide (TiO₂) and subsequent chemical reduction to obtain SS-derived black TiO₂ (SS/bTiO₂) material. As-prepared SS/bTiO₂ is mixed with transparent paint to prepare hydrophilic black paints and applied to a glass substrate using a spray gun. SS/bTiO₂-based paint shows similar blackness (L^*=15.7) compared to commercial carbon black-based paint, and remarkable NIR reflectance (26.5R%, 905nm). Furthermore, MEMS and Rotating LiDAR have successfully detected the SS/bTiO₂-based paint. This is attributed to the occurrence of high reflection of light at the interface between the black TiO₂ and the silicon sludge according to the Fresnel's reflection principle. Hence, the new application field to effectively recycle silicon sludge generated in the semiconductor manufacturing process has been presented.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.243-248
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• 2017

Sea ice which is an important component of the global climate system is being actively detected by satellite because it have been distributed to polar and high-latitude region. and the sea ice detection method using satellite uses reflectance and temperature data. the sea ice detection method of Moderate-Resolution Imaging Spectroradiometer (MODIS), which is a technique utilizing Ice Surface Temperature (IST) have been utilized by many studies. In this study, we propose a simple and effective method of sea ice detection using the dynamic threshold technique with no IST calculation process. In order to specify the dynamic threshold, pixels with freezing point of MODIS IST of 273.0 K or less were extracted. For the extracted pixels, we analyzed the relationship between MODIS IST, MODIS $11{\mu}m$ channel brightness temperature($T_{11{\mu}m}$) and Brightness Temperature Difference ($BTD:T_{11{\mu}m}-T_{12{\mu}m}$). As a result of the analysis, the relationship between the three values showed a linear characteristic and the threshold value was designated by using this. In the case ofsea ice detection, if $T_{11{\mu}m}$ is below the specified threshold value, it is detected as sea ice on clear sky. And in order to estimate the performance of the proposed sea ice detection method, the accuracy was analyzed using MODIS Sea ice extent and then validation accuracy was higher than 99% in Producer Accuracy (PA).