• 환경영향평가
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• 제18권6호
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• pp.387-392
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• 2009

As Our society's environmental awareness and concern the significant increases, the importance of the legal system for environmental conservation such as the Prior Environmental Review System, Environmental Impact Assessment is growing increasingly. but, still critical issues are present such as reliability. Though there could be various causes such as the system or procedures etc. Above all, basically the environmental data problem is the critical cause. Therefore, this study was trying to improve the environmental data accuracy using the high-resolution color aerial photography, LiDAR data and Object Oriented Classification method. And in this study, classification based on coverage percentage of a particular species was attempted through the multi-resolution segmentation and multi-level classification method. The classification result was verified by comparison with 11 points local survey data. All 11 points were classified correctly. And even though the exact coverage percentage of the particular species did not be measured, It was confirmed that the species was occupied similar portion. It is important that the environmental data which can be used for the conservation value assessment could be acquired.

• 한국군사과학기술학회지
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• 제13권5호
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• pp.894-902
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• 2010

This paper presents a vision-based robust off-road terrain classification method against environmental variation. As a supervised classification algorithm, we applied a neural network classifier using wavelet features extracted from wavelet transform of an image. In order to get over an effect of overall image feature variation, we adopted environment sensors and gathered the training parameters database according to environmental conditions. The robust terrain classification algorithm against environmental variation was implemented by choosing an optimal parameter using environmental information. The proposed algorithm was embedded on a processor board under the VxWorks real-time operating system. The processor board is containing four 1GHz 7448 PowerPC CPUs. In order to implement an optimal software architecture on which a distributed parallel processing is possible, we measured and analyzed the data delivery time between the CPUs. And the performance of the present algorithm was verified, comparing classification results using the real off-road images acquired under various environmental conditions in conformity with applied classifiers and features. Experiments show the robustness of the classification results on any environmental condition.

• 한국산업보건학회지
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• 제22권2호
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• pp.140-148
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• 2012

Objectives: This study was performed to provide harmonized guidelines on health and environmental classification criteria and its results of chemicals in Korea. Methods: Firstly, The history of GHS implementation in UN and Korea was reviewed. Secondly, the differences in classification criteria on health and environmental hazards among UN GHS and two Korean government agencies, Korea Ministry of Employment and Labour (KMoEL) and Korea Ministry of Environmental (KMoE). The classification results were compared between classifications of Korea Occupational Safety and Health Agency (KOSHA) based on KMoEL and classifications of Korea National Institute of Environmental Research (KNIER) based on KMoE. Finally, an inter-agency harmonization on the classification criteria and the results was suggested by comparing the classification results of 5 chemicals; Benzene, carbon disulfide, formaldehyde, toluene-2,4-diisocyanate, and trichloroethylene. Results: KMoEL and KMoE revised regulations on chemical management and published a Notices on GHS classification criteria according to UN GHS document. However, the hazard to the ozone layer contained in the latest edition of UN GHS document published in 2011 was not included yet. The differences in classifications of 5 chemicals between KOSHA and KNIER were 36.2% in health hazards and 23.4% in environmental hazards, respectively. In conclusion, we suggested that a new revision be needed to include newly contained hazard and inter-agency working party be organized to harmonize classification results.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.361-364
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• 2008

It is well known that combining spatial and spectral information can improve land use classification from satellite imagery. High spatial resolution classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, extracting the spatial information is one of the most important steps in high resolution satellite image classification. In this paper, we propose a new spatial feature extraction method. The extracted features are integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a Support Vector Machines classifier. In order to evaluate the proposed feature extraction method, we applied our approach to KOMPSAT-2 data and compared the result with the other methods.

• 한국산학기술학회논문지
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• 제5권1호
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• pp.87-92
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• 2004

This review focuses on the developed and the being developed environmental sensors in particular biological sensors. As well as discussing the classification and some main principles, presenting current trend of the environmental sensors is given. Two main categories are immunosensors and catalytic sensors. In addition to those. DNA or RNA sensors or protein based sensors are discussed. Some crucial examples of the applications of such sensors are given to show how the sensor technology it used for environmental and biological monitoring, biomarkers of exposure.

• 한국환경복원기술학회지
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• 제20권3호
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• pp.1-18
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• 2017

This study constructed a high-resolution bioclimatic classification map of South Korea which classifies land into homogeneous zones by similar environment properties using advanced statistical techniques compared to existing ecological area classification studies. The climate data provided by WorldClim(1960-1990) were used to generate 27 bioclimatic variables affecting biological habitats, and key environmental variables were derived from Correlation Analysis and Principal Component Analysis. Clustering Analysis was performed using the ISODATA method to construct a 30'(~1km) resolution bioclimatic classification map. South Korea was divided into 21 regions and the results of classification were verified by correlation analysis with the Gross Primary Production(GPP), Actual Vegetation map made by the Ministry of Environment. Each zones' were described and named by its environmental characteristics and major vegetation distribution. This study could provide useful spatial frameworks to support ecosystem research, monitoring and policy decisions.

• Geomechanics and Engineering
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• 제35권1호
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• pp.67-80
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• 2023

Due to the nature of the conjunctive Cone Penetration Test(CPT), which does not verify the actual sample directly, geotechnical engineers commonly classify the underground geomaterials using CPT results with the classification diagrams proposed by various researchers. However, such classification diagrams may fail to reflect local geotechnical characteristics, potentially resulting in misclassification that does not align with the actual stratification in regions with strong local features. To address this, this paper presents an objective method for more accurate local CPT soil classification criteria, which utilizes C4.5 decision tree models trained with the CPT results from the clay-dominant southern coast of Korea and the sand-dominant region in South Carolina, USA. The results and analyses demonstrate that the C4.5 algorithm, in conjunction with oversampling, outlier removal, and pruning methods, can enhance and optimize the decision tree-based CPT soil classification model.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.107-109
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• 2003

Optical sensor-based land cover categories produce spectral signature confusion along with degraded classification accuracy. In the classification tasks, the goal of fusing data from different sensors is to reduce the classification error rate obtained by single source classification. This paper describes the result of land cover/land use classification derived from solely of Landsat TM (TM) and multisensor image fusion between JERS 1 SAR (JERS) and TM data. The best radar data manipulation is fused with TM through various techniques. Classification results are relatively good. The highest Kappa Coefficient is derived from classification using principal component analysis-high pass filtering (PCA+HPF) technique with the Overall Accuracy significantly high.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.198-200
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• 2003

This study proved if the high resolution satellite imagery of IKONOS is suitable for preparing digital forest cover map. Three methods, the pixel based classification with maximum likelihood (PML), the segment based classification with majority principle(SMP), and the segment based classification with maximum likelihood(SML), were applied to classify and delimitate forest cover of IKONOS imagery taken in May 2000 in a forested area in the central Korea. The segment-based classification was more suitable for classifying and deliminating forest cover in Korea using IKONOS imagery. The digital forest cover map in which each class is delimitated in the form of a polygon can be prepared on the basis of the segment-based classification.

• 대한원격탐사학회지
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• 제23권3호
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• pp.153-160
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• 2007

Forest cover density studies using high resolution satellite data and object oriented classification are limited in India. This article focuses on the potential use of QuickBird satellite data and object oriented classification in forest density mapping. In this study, the high-resolution satellite data was classified based on NDVI/pixel based and object oriented classification methods and results were compared. The QuickBird satellite data was found to be suitable in forest density mapping. Object oriented classification was superior than the NDVI/pixel based classification. The Object oriented classification method classified all the density classes of forest (dense, open, degraded and bare soil) with higher producer and user accuracies and with more kappa statistics value compared to pixel based method. The overall classification accuracy and Kappa statistics values of the object oriented classification were 83.33% and 0.77 respectively, which were higher than the pixel based classification (68%, 0.56 respectively). According to the Z statistics, the results of these two classifications were significantly different at 95% confidence level.