• The Korean Journal of Air & Space Law and Policy
• /
• no.spc
• /
• pp.59-86
• /
• 2007

The purpose of this paper is to suggest to tackle the problem of poor cooperation in space activities, by re-examining the nature of the competitive political environment, and by building up a normative overarching framework, One of the most acute problems that hampers regional cooperation is the U.S. influence as represented in the MTCR, a supplier's cartel, as was evidenced in the ill-fate of the 2001 launch contract between China and Korea the next year. Notably China, the third space power in the world, has not been allowed to join the MTCR despite her application in June 2004. A possible reconciliation between China and the MTCR over her application for a partnership would set a cornerstone in building up a cooperative environment in the Northeast Asia. Just as the Helsinki process was an overarching norm building framework, comprising human rights, security and environmental issues, it would be desirable that a future peace framework in Northeast Asia dealing with the pending issues of Korean peninsula should also comprise of such broad issues as one relating to cooperation in space activities in the region. South Korea could tap expertise from her neighbor China. When South Korea become an independent space power either with her own technology or otherwise, she would be in a better position to play a role as a balancer in coordinating between the two neighboring space giants. It is remarkable that the Japanese led APRSAT has contributed much in establishing Sentinel Asia as a part of the Disaster Management Scheme, in that each participant, whether it be a state agency, or a private entity like a university or a research institute, can tap the common data to contribute to the common good of safety.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.sup3
• /
• pp.275-278
• /
• 2016

The membrane epithelial mucin MUC1 is expressed at the luminal surface of most simple epithelial cells, but expression is greatly increased in most breast cancers. The aims of present study were to investigate expression of the MUC1 gene and interactive affects in metastases. Whole cell RNA isolation from 50 sentinel lymph nodes (SNLs) of breast cancer patients was performed using reverse transcription and real-time PCR. All patients were diagnosed with breast cancer and without metastasis, confirmed by IHC staining. The evaluation of tumor and normal samples for expression of MUC1 gene, the results were 49.1% non-expressive and 45.3% expression (Student t, p = 0.03). Also in comparison of normal breast tissue and breast cancer SLN for MUC1 gene, MUC1 negative SLNs were 75.0% (18 samples) and MUC1 positive samples were 25.0% (6 samples). Over-expression of MUC1 gene may offer a target for therapy related to progression and metastasis in women with breast cancer.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_4
• /
• pp.1327-1339
• /
• 2019

Surface reflectance obtained by absolute atmospheric correction from satellite images is useful for scientific land applications and analysis ready data (ARD). For Landsat and Sentinel-2 images, many types of radiometric processing methods have been developed, and these images are supported by most commercial and open-source software. However, in the case of KOMPSAT 3/3A images, there are currently no tools or open source resources for obtaining the reflectance at the top-of-atmosphere (TOA) and top-of-canopy (TOC). In this study, the atmospheric correction module of KOMPSAT 3/3A images is newly implemented to the optical calibration algorithm supported in the Orfeo ToolBox (OTB), a remote sensing open-source tool. This module contains the sensor model and spectral response data of KOMPSAT 3A. Aerosol measurement properties, such as AERONET data, can be used to generate TOC reflectance image. Using this module, an experiment was conducted, and the reflection products for TOA and TOC with and without AERONET data were obtained. This approach can be used for building the ARD database for surface reflection by absolute atmospheric correction derived from KOMPSAT 3/3A satellite images.

• Journal of Gastric Cancer
• /
• v.5 no.3 s.19
• /
• pp.186-190
• /
• 2005

Purpose: Intraoperative assessment of lymph node status is important when performing limited surgery in gastric cancer patients. Currently available techniques are frozen section, imprint cytology, and other molecular methods, and most current studies use the frozen section method. In the present study, the authors focused on the accuracy and the feasibility of imprint cytology as a tool to assess the lymph node status intraoperatively in gastric cancer surgery. Materials and Methods: Between April 2001 and March 2003, we performed imprint cytology of the sentinel nodes of 260 consecutive patients. After review by an experienced cytopathologist, the sensitivity, the specificity and the overall accuracy were determined. Results: The time required for intraoperative imprint cytology was 8 minutes, and the sensitivity, the specificity and the overall accuracy were 52.2%, 88.8%, and 73.8%, respectively. Conclusion: Imprint cytology can be a useful technique for assessing lymph node status intraoperatively if the sensitivity and the specificity can be improved to an acceptable level.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.3
• /
• pp.187-194
• /
• 2017

The present study introduces a method to parallelize k-means clustering algorithm for fast unsupervised classification of large satellite imagery. Known as a representative algorithm for unsupervised classification, k-means clustering is usually applied to a preprocessing step before supervised classification, but can show the evident advantages of parallel processing due to its high computational intensity and less human intervention. Parallel processing codes are developed by using multi-threading based on OpenMP. In experiments, a PC of 8 multi-core integrated CPU is involved. A 7 band and 30m resolution image from LANDSAT 8 OLI and a 8 band and 10m resolution image from Sentinel-2A are tested. Parallel processing has shown 6 time faster speed than sequential processing when using 10 classes. To check the consistency of parallel and sequential processing, centers, numbers of classified pixels of classes, classified images are mutually compared, resulting in the same results. The present study is meaningful because it has proved that performance of large satellite processing can be significantly improved by using parallel processing. And it is also revealed that it easy to implement parallel processing by using multi-threading based on OpenMP but it should be carefully designed to control the occurrence of false sharing.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.871-884
• /
• 2021

The purpose of this study was to determine ways to increase efficiency in constructing and verifying artificial intelligence learning data on land cover using aerial and satellite images, and in applying the data to AI learning algorithms. To this end, multi-resolution datasets of 0.51 m and 10 m each for 8 categories of land cover were constructed using high-resolution aerial images and satellite images obtained from Sentinel-2 satellites. Furthermore, fine data (a total of 17,000 pieces) and coarse data (a total of 33,000 pieces) were simultaneously constructed to achieve the following two goals: precise detection of land cover changes and the establishment of large-scale learning datasets. To secure the accuracy of the learning data, the verification was performed in three steps, which included data refining, annotation, and sampling. The learning data that wasfinally verified was applied to the semantic segmentation algorithms U-Net and DeeplabV3+, and the results were analyzed. Based on the analysis, the average accuracy for land cover based on aerial imagery was 77.8% for U-Net and 76.3% for Deeplab V3+, while for land cover based on satellite imagery it was 91.4% for U-Net and 85.8% for Deeplab V3+. The artificial intelligence learning datasets on land cover constructed using high-resolution aerial and satellite images in this study can be used as reference data to help classify land cover and identify relevant changes. Therefore, it is expected that this study's findings can be used in the future in various fields of artificial intelligence studying land cover in constructing an artificial intelligence learning dataset on land cover of the whole of Korea.

• Journal of Cadastre & Land InformatiX
• /
• v.48 no.2
• /
• pp.171-183
• /
• 2018

Despite the rapid expansion of satellite images supply, the application of imagery is often restricted due to unautomated image processing. This paper presents the automated process for the selection of training areas which are essential to conducting supervised image classification. The training areas were selected based on the prior and cover information. After the selection, the training data were used to classify land cover in an urban area with the latest image and the classification accuracy was valuated. The automatic selection of training area was processed with following steps, 1) to redraw inner areas of prior land cover polygon with negative buffer (-15m) 2) to select the polygons with proper size of area ($2,000{\sim}200,000m^2$) 3) to calculate the mean and standard deviation of reflectance and NDVI of the polygons 4) to select the polygons having characteristic mean value of each land cover type with minimum standard deviation. The supervised image classification was conducted using the automatically selected training data with Sentinel-2 images in 2017. The accuracy of land cover classification was 86.9% ($\hat{K}=0.81$). The result shows that the process of automatic selection is effective in image processing and able to contribute to solving the bottleneck in the application of imagery.

• Journal of Korea Water Resources Association
• /
• v.51 no.12
• /
• pp.1181-1194
• /
• 2018

This study suggests a novel approach of estimating stream flow discharge using the Synthetic Aperture Radar (SAR) images taken from 2015 to 2017 by European Space Agency Sentinel-1 satellite. Fifteen small to medium sized rivers in the Han River basin were selected as study area, and the SAR satellite images and flow data from water level and flow observation system operated by the Korea Institute of Hydrological Survey were used for model construction. First, we apply the histogram matching technique to 12 SAR images that have undergone various preprocessing processes for error correction to make the brightness distribution of the images the same. Then, the flow estimation model was constructed by deriving the relationship between the area of the stream water body extracted using the threshold classification method and the in-situ flow data. As a result, we could construct a power function type flow estimation model at the fourteen study areas except for one station. The minimum, the mean, and the maximum coefficient of determination ($R^2$) of the models of at fourteen study areas were 0.30, 0.80, and 0.99, respectively.

• Journal of Cadastre & Land InformatiX
• /
• v.48 no.2
• /
• pp.137-151
• /
• 2018

Recently, National interest has been rising due to earthquakes in Gyeongju and Pohang, disasters caused by landslides, landslides, and sinkholes around construction sites, and damage caused by disasters. SAR is able to detect ground displacement in mm for wide area, collect data through satellite, predict timeliness of crustal change by time series analysis, and reduce disaster and disaster damage. The purpose of this study is to investigate the latest SAR interference analysis technique (SqueeSAR analysis technique) of Sentinel-1A satellite (SAR sensor) of European ESA for about 3 years by selecting the 1st landfill site in the metropolitan area in Incheon, The settlement amount was calculated in a time series. Especially, in order to examine the accuracy of the subsidence and subsidence tendency by the SqueeSAR analysis method, the ground level survey was compared and analyzed for the first time in Korea. Also, the tendency of the subsidence trend was predicted by analyzing the time series and correlation trend of the subsidence for three years. Through this study, it is expected that disaster prevention and disaster prevention such as sinkhole and landslide can be utilized from time series monitoring of crustal variation of the land.

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