• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of the Korean Geographical Society
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• v.37 no.4
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• pp.293-316
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• 2002

This study aims to identify changes that have occurred in the regional structure and locational characteristics of the Najin-Seonbong Economic and Trade Zone established in North Korea in 1991. In order to analyze land use patterns as variables of change in the regional structure, an field trip data, satellite imagery and other materials about the region are examined. In terms of its location as a major regional transit hub, the Najin-seonbong Economic and Trade Zone has not been supported by the required infrastructural developments and the establishment of the export processing zones has exposed the lack of vital links with local networks and industry. Thus, despite the fact that the local government has made a lot of effort in attracting foreign investment over the past decade, little progress has been made and the region has not changed. By and large, its operational efficiency and potential for development as a major export processing zone has been relatively limited. In the long w, prospects for the region's emergence as a major economic player will depend on the North Korean Govemment's policy in tackling the various infrastructural deficiencies.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.487-501
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• 2020

As a viable option for retrieval of LST (Land Surface Temperature), this paper presents a DNN (Deep Neural Network) based approach using 148 Landsat 8 images for South Korea. Because the brightness temperature and emissivity for the band 10 (approx. 11-㎛ wavelength) of Landsat 8 are derived by combining physics-based equations and empirical coefficients, they include uncertainties according to regional conditions such as meteorology, climate, topography, and vegetation. To overcome this, we used several land surface variables such as NDVI (Normalized Difference Vegetation Index), land cover types, topographic factors (elevation, slope, aspect, and ruggedness) as well as the T₀ calculated from the brightness temperature and emissivity. We optimized four seasonal DNN models using the input variables and in-situ observations from ASOS (Automated Synoptic Observing System) to retrieve the LST, which is an advanced approach when compared with the existing method of the bias correction using a linear equation. The validation statistics from the 1,728 matchups during 2013-2019 showed a good performance of the CC=0.910~0.917 and RMSE=3.245~3.365℃, especially for spring and fall. Also, our DNN models produced a stable LST for all types of land cover. A future work using big data from Landsat 5/7/8 with additional land surface variables will be necessary for a more reliable retrieval of LST for high-resolution satellite images.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.10
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• pp.709-721
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• 2006

The traditional broadcasting services over terrestrial, satellite and cable media have been unidirectional mass media regardless of TV viewer's preferences. Recently ich media streaming has become possible via the broadb and networks. Furthermore, since bidirectional communication is possible, personalcasting such as personalized streaming services has been emerging by taking into account the user's preference on content genres, viewing times and actors/actresses etc. Accordingly, personal media becomes an important means for content provision service in addition to the traditional broadcasting service as mass media. In this paper, we introduce a user profile reasoning method for target advertisement which is considered an important application in personalcasting service. The proposed user profile reasoning method predicts an unknown TV viewer's gender and ages by analyzing TV Viewing history data. Based on the estimated user's gender and ages, a target advertisement is provided with TV Anytime metadata. A proposed target advertisement system is developed based on the user profile reasoning and the target advertisement selection method. To show the effectiveness of our proposed methods, we present a plenty of experimental results by using realistic TV viewing history data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.658-663
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• 2018

Current measuring methods for air quality are based on ground measurement networks and satellite data. New methods of collecting evidence with advanced sensors are needed because current methods have limitations in collecting evidence for the illegal emission of air pollutants at narrow areas or specific sites. This study analyzed the possibility of using an ultraviolet hyperspectral sensor to measure the concentration of nitrogen dioxide and sulfur dioxide. Two types of spectra were used: simulated spectra for gases with various concentrations using a radiative transfer model and observed spectra for each gas for a concentration. To understand the possibility of using a hyperspectral sensor, the differences between the simulated spectra and the observed spectra were analyzed, and the variation of simulated spectra were then analyzed according to the concentration. The results showed good agreement between observed spectra and simulated spectra. In addition, the absorption depth at specific wavelengths in the simulated spectra had a very strong correlation with the gas concentration. The gas concentration could be estimated using the hyperspectral sensor. In the future, validation would be needed to estimate the gas concentration through observations of various concentrations of gases using a hyperspectral sensor.

• Korean Journal of Agricultural and Forest Meteorology
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• v.5 no.2
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• pp.138-149
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• 2003

Korean regional network of tower flux sites, KoFlux, has been initiated to better understand $CO_2$, water and energy exchange between ecosystems and the atmosphere, and to contribute to regional, continental, and global observation networks such as FLUXNET and CEOP. Due to heterogeneous surface characteristics, most of KoFlux towers are located in non-ideal sites. In order to quantify carbon and energy exchange and to scale them up from plot scales to a region scale, applications of various methods combining measurement and modeling are needed. In an attempt to infer regional-scale flux, four methods (i.e., tower flux, convective boundary layer (CBL) budget method, MM5 mesoscale model, and NCAR/NCEP reanalysis data) were employed to estimate sensible heat flux representing different surface areas. Our preliminary results showed that (1) sensible heat flux from the tower in Haenam farmland revealed heterogeneous surface characteristics of the site; (2) sensible heat flux from CBL method was sensitive to the estimation of advection; and (3) MM5 mesoscale model produced regional fluxes that were comparable to tower fluxes. In view of the spatial heterogeneity of the site and inherent differences in spatial scale between the methods, however, the spatial representativeness of tower flux need to be quantified based on footprint climatology, geographic information system, and the patch scale analysis of satellite images of the study site.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.39-52
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• 2006

As accelerates the technical evolution of high-speed network and progresses the digitalization of broadcasting network, TV channel service through satellite/cable/terrestrial networks becomes more stable and mature. However, TV channel service using IP network such as IPTV is recently emerging. Especially, when it comes to current mosaic EPG(Electronic Program Guide) as a channel guide, the implementation of EPG via IP network is under developing. Furthermore, the personal target mosaic EPG is not provided at all in the IPTV. This paper proposes a construction method of mosaic system which can support fixed and personalized mosaic EPG using IP network for viewers. The fixed mosaic EPG is made several steps as follows ; First, H/E generates several mosaic A/V streams. Then, which are transmitted to the STB in terms of multicasting via IP network. Finally, mosaic EPG is displayed on TV through STB. In addition, this paper describes a construction model of the personalized A/V mosaic EPG that represents each person's favorite channels according to their tastes and interests. As for the contributions. The TV channel guide using IP network enable viewer to select channel more easily with practical adaptation of multi-channel expansibility and sufficient usability. In addition, through personal mosaic EPG, a number of viewers can compose their own mosaic EPG and enjoy a variety of channel easily in accordance with their preferences. Finally, the personal mosaic EPG can prohibit non-adult users from connecting adult-only contents more efficiently.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1399-1414
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• 2018

Natural forests are un-manned forests where the artificial forces of people are not applied to the formation of forests. On the other hand, artificial forests are managed by people for their own purposes such as producing wood, preventing natural disasters, and protecting wind. The artificial forests enable us to enhance economical benefits of producing more wood per unit area because it is well-maintained with the purpose of the production of wood. The distinction surveys have been performed due to different management methods according to forests. The distinction survey between natural forests and artificial forests is traditionally performed via airborne remote sensing or in-situ surveys. In this study, we suggest a classification method of forest types using satellite imagery to reduce the time and cost of in-situ surveying. A classification map of natural forest and artificial forest were generated using KOMPSAT-3, 3A, 5 data by employing artificial neural network (ANN). And in order to validate the accuracy of classification, we utilized reference data from 1/5,000 stock map. As a result of the study on the classification of natural forest and plantation forest using artificial neural network, the overall accuracy of classification of learning result is 77.03% when compared with 1/5,000 stock map. It was confirmed that the acquisition time of the image and other factors such as needleleaf trees and broadleaf trees affect the distinction between artificial and natural forests using artificial neural networks.

• Industry Promotion Research
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• v.6 no.2
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• pp.1-10
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• 2021

The content of the information age does not simply convey content but includes all the transactions that arise from its interaction. The types and forms of information being traded through this interaction are recognized differently from the contents that have been passed on to mankind so far by creating new meaningful content. Because the distribution of interactive content transcending the concept of time-to-time in the network environment is an important component of access to added value in the new world, unlike the content of simple concepts seen in the products of communication so far. In this study, the purpose of this study is to recognize the current status and problems of the Korean digital content industry and to seek ways to revitalize the Korean digital content industry to lead the global market in the future. Specifically, first, we want to learn about the concept of digital content. Second, we would like to look at the industrial trends of digital content at home and abroad. Third, we present a plan to streamline digital content. Fourth, derive research results and implications. In this work, the following results are derived: First, in order for Korea to enter a digital content powerhouse, each government department must first break away from the selfishness of the ministry and actively cooperate to efficiently establish and implement various policies. Second, e-books should be introduced just as current paper and CD-ROM titles are exempt from VAT, and security solutions, related technology development, and copyright issues should be urgently addressed to revitalize the market. Third, the demand for high-quality content should increase as information infrastructure such as high-speed information and communication networks and satellite broadcasting is established.