• 대한원격탐사학회지
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• 제29권5호
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• pp.537-544
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• 2013

남극 세종 과학 기지가 위치하고 있는 바톤반도는 눈과 식생이 주를 이루고 있고, 기후변화와 같은 환경변화에 민감하게 반응한다. 극지역의 지표 모니터링은 기후변화 이해를 위해 중요하다. 그러나 극 지역은 접근성 및 공간규모로 인해 지속적으로 모니터링 하기에 어려움이 있다. 위성영상은 지속적으로 동일지역을 모니터링 할 수 있다는 장점과 함께 다중분광영역을 이용하여 지표의 상태를 파악하는데 효율적이다. 따라서 본 연구에서는 바톤반도의 지표의 상태를 지속적으로 모니터링하기 위한 기초자료로 KOMPSAT-2 다중 분광 위성영상을 이용하여 토지피복분류를 수행하였고, 나아가 분류된 토지피복 중 식생 종의 분포를 파악하였다. 다중분광영상인 KOMPSAT-2 위성영상과 현장관측자료를 이용하여 계층적 분류를 수행하였고 정확도를 평가하였다. 전반적으로 식생지역과 비식생 지역이 명확하게 분류되었으나 식생 종 분류에는 낮은 정확도를 보였다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1248-1248
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• 2001

Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and considerable compositional changes in milk, reducing milk quality. The potential of near infrared (NIR) spectroscopy in the region from 1100 to 2500nm and chemometric method for classification to detect milk from mastitic cows was investigated. A total of 189 milk samples from 7 Holstein cows were collected for 27 days, consecutively, and analyzed for somatic cells (SCC). Three of the cows were healthy, and the rest had mastitis periods during the experiment. NIR transflectance milk spectra were obtained by the InfraAlyzer 500 spectrophotometer in the spectral range from 1100 to 2500nm. All samples were divided into calibration set and test set. Class variable was assigned for each sample as follow: healthy (class 1) and mastitic (class 2), based on milk SCC content. The classification of the samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two concentration of SCC - 200 000 cells/ml and 300 000 cells/ml, respectively, were used as thresholds fer separation of healthy and mastitis cows. The best detection accuracy was found for models, obtained using 200 000 cells/ml as threshold and smoothed absorbance data - 98.41% from samples in the calibration set and 87.30% from the samples in the independent test set were correctly classified. SIMCA results for classes, based on 300 000 cells/ml threshold, showed a little lower accuracy of classification. The analysis of changes in the loading of first PC factor for group of healthy milk and group of mastitic milk showed, that separation between classes was indirect and based on influence of mastitis on the milk components. The accuracy of mastitis detection by SIMCA method, based on NIR spectra of milk would allow health screening of cows and differentiation between healthy and mastitic milk samples. Having SIMCA models, mastitis detection would be possible by using only DIR spectra of milk, without any other analyses.

• 한국환경복원기술학회지
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• 제4권2호
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• pp.36-44
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• 2001

The main purpose of this study was to classify forest vegetation effectively using Landsat Thematic Mapper data(June, 1994) in mountainous region. The research area was the Mt. Wolak Experimental Forest of Chungbuk National University, near Chungju and Jecheon city, Chungcheongbuk-do. To classify forest vegetation effectively, Normalized Difference Vegetation Index(NDVI) was used to reduce topographic effects. This NDVI was modified and transformed to the value of 0 to 255, and then the modified values were combined with other Landsat Thematic Mapper bands. To classify forest and land cover types, unsupervised classification method was used. The results of this study are summarized as follows. 1. Combinations of band "3, 5, NDVI" in Landsat Thematic Mapper data showed a good separation with high accuracy. The expected classification accuracy was 95.1% in Landsat Thematic Mapper data. 2. The Land Cover types were classified into six groups : coniferous forest, deciduous forest, mixed forest, paddy and grass, non-forest, and other undetectable areas. As these classified results were compared with the reconnaissance survey and aerial black and white infrared photographs, the overall classification accuracy was 76.5% in Landsat Thematic Mapper data. 3. The portion of non-forest in Mt. Wolak area was 1.9%. The percentages of coniferous, deciduous and mixed forests were 30.9%, 35.7% and 26.4%, respectively. 4. As these classified results were compared with other reference data, the percentages of coniferous, deciduous and mixed forests increased, but the portion of non-forest was exceedingly diminished. These differences are thought to be from the different research method and the different season of received Landsat Thematic Mapper data.

• 한국산학기술학회논문지
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• 제9권5호
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• pp.1210-1217
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• 2008

사상의학은 개인별 체질의 분류에 따른 맞춤형 의학으로 우리나라 고유의 독특한 전통 의학이다. 이와 같은 사상의학에서 가장 중요하게 여겨지는 것이 사상체질의 정확한 분류이다. 따라서 사상체질 분류에 대한 객관적 요소의 확보 및 진단 지표 마련이 시급하게 해결되어야 할 과제이다. 이를 위해 본 논문에서는 사상체질 분류의 객관화, 정량화 및 시각화를 위해 얼굴 영상 신호와 음성 신호를 분석하여 결과값을 추출하고 체질별 집단군간의 차이점을 비교하여 사상체질 분류 시스템을 구현하고자 한다. 특히 영상 및 음성 신호는 성별, 연령별, 지역별 등의 구분에 따라 달라지기 때문에 본 논문에서는 40에서 50대 사이의 장년 여성을 대상으로 서울지역 거주자에 한해 사상체질 집단군을 구성하고 이들의 영상 및 음성 신호를 추출하여 체질간 비교, 분석을 수행하고자 한다. 최종적으로 실험을 통한 연구 결과의 유의성을 입증하고자 한다.

• 한국멀티미디어학회논문지
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• 제11권11호
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• pp.1615-1623
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• 2008

이 논문에서는 곤충 발자국의 패턴을 찾아 개체를 인식하기 위해서, 개선된 SOM 알고리즘과 ART2 알고리즘을 사용하여 인식의 기본 영역을 추출한다. 또한 Trace 변환을 이용하여 발자국의 인식에 필요한 특징을 추출하고 개체를 판단하는 기법을 제안한다. 제안한 기법에서는 모폴로지 기법을 이용하여 region을 먼저 찾고, 개선된 SOM과 ART2 알고리즘을 이용하여 곤충의 크기와 종류에 관계없이 세그먼트를 추출한다. 그리고 곤충 발자국과 같이 다양한 변형이 존재하는 패턴에 적합한 특징값을 찾기 위해서 Trace 변환을 이용하고, 함수의 조합으로 재구성된 Triple 특징값을 이용하여 곤충별로 고유한 패턴을 찾아 인식 실험을 수행한다. 곤충 발자국에서 명확한 발자국과 그렇지 못한 발자국을 자동으로 결정하는 것이 매우 어렵다. 따라서 이와 같이 불확실한 대상을 제외시키지 않고 가능성의 대상으로 판단하고 분류하기 위해서 퍼지 가중치 평균을 이용하여 인식을 수행한다. 제안한 방법에 의한 곤충 발자국의 영역 추출과 인식 실험을 실시하고 그 결과를 제시하였다.

• 정보처리학회논문지B
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• 제16B권5호
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• pp.377-384
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• 2009

본 논문은 지역적 엔트로피 기반의 히스토그램을 이용한 문서영상의 분할과 텍스처 기반의 주성분 분석을 이용한 구성요소인 글자, 그림, 그래프 등의 구성요소 분류방안을 제안한다. 지역적 엔트로피와 히스토그램을 이용함으로써 문서영상의 다양한 변형이나 잡음에 강건하며 빠르고 손쉬운 이진화가 가능하다. 그리고 문서영상 내 존재하는 구성요소들이 각기 다른 텍스처 정보를 가지고 있다는 것에 착안하여 각 분할 영역의 텍스처 정보를 기반으로 주성분분석을 수행하였으며 이를 통해 사전에 구성요소들에 대한 구조정보를 설정할 필요가 없다는 장점을 가진다. 실험결과에서 다양한 문서영상의 분할 및 분류결과를 보였으며, 기존 방법보다 우수한 성능을 가져 그 유효함을 보였다.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2004년도 GIS/RS 공동 춘계학술대회 논문집
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• pp.493-498
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• 2004

The objective of the present study is to compare various conventional and recently evolved satellite image-processing techniques and to ascertain the best possible technique that can identify and position of aquaculture farms accurately in and around the Younggwang coastal area. Several conventional techniques performed to extract such information fiom the Landsat-TM imagery do not seem to yield better information about the aquaculture farms, and lead to misclassification. The large errors between the actual and extracted aquaculture farm information are due to existence of spectral confusion and inadequate spatial resolution of the sensor. This leads to possible occurrence of mixture pixels or 'mixels' of the source of errors in the classification techniques. Understanding the confusing and mixture pixel problems requires the development of efficient methods that can enable more reliable extraction of aquaculture farm information. Thus, the more recently evolved methods such as the step-by-step partial spectral end-member extraction and linear spectral unmixing methods are introduced. The farmer one assumes that an end-member, which is often referred to as 'spectrally pure signature' of a target feature, does not appear to be a spectrally pure form, but always mix with the other features at certain proportions. The assumption of the linear spectral unmxing is that the measured reflectance of a pixel is the linear sum of the reflectance of the mixture components that make up that pixel. The classification accuracy of the step-by-step partial end-member extraction improved significantly compared to that obtained from the traditional supervised classifiers. However, this method did not distinguish the aquaculture ponds and non-aquaculture ponds within the region of the aquaculture farming areas. In contrast, the linear spectral unmixing model produced a set of fraction images for the aquaculture, water and soil. Of these, the aquaculture fraction yields good estimates about the proportion of the aquaculture farm in each pixel. The acquired proportion was compared with the values of NDVI and both are positively correlated (R$^2$ =0.91), indicating the reliability of the sub-pixel classification.ixel classification.

• 한국재료학회지
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• 제19권4호
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• pp.212-219
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• 2009

A study on the dry beneficiation of sericite occurring in the Daehyun Mine of the Republic of Korea region as performed by applying selective grinding and air classification techniques. Quartz and sericite occurred in the raw ore as major components. The results of liberation using a ball mill and an impact mill showed that the contents of $R_2O$ were increased while $SiO_2$ was decreased in proportion to decreasing particle size. According to the XRD, XRF analysis and the EDS of SEM analysis, the ball mill gave a better grade product in $R_2O$ content than the impact mill when the particle size was the same. When the raw ore was ground by the impact mill with arotor speed 57.6 m/sec and then followed by 15,000rpm classification using an air classifier, the chemical composition of the over flowed product was 49.65wt% $SiO_2$, 32.15wt% $Al_2O_3$, 0.13wt% $Fe_2O_3$, 10.37wt% $K_2O$, and 0.14wt% $Na_2O$. This result indicates that the $R_2O$ contents were increased by 49.5% compared to that of the raw ore. From these results described above, it is suggested that hard mineral such as Quartz little ground by selective grinding using impact mill whereas soft mineral such as sericite easily ground to small size. As a result of that hard minerals can be easily removed from the finely ground sericite by air classification and the $R_2O$ grade of thus obtained concentrate was improved to higher than 10wt% which can be used for ceramics raw materials.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.426-428
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• 2003

Optical and microwave remote sensing data have been widely used in land cover and land use classification. Thanks to the spectral absorption characteristics of ground object in visible and near infrared region, optical data enables to extract different land cover types according to their material composition like water body, vegetation cover or bare land. On the other hand, microwave sensor receives backscatter radiance which contains information on surface roughness, object density and their 3-D structure that are very important complementary information to interpret land use and land cover. Separate use of these data have brought many successful results in practice. However, the accuracy of the land use / land cover established by this methodology still has some problems. One of the way to improve accuracy of the land use / land cover classification is just combination of both optical and microwave data in analysis. In this paper for the research, the author used LANDSAT TM scene 127/45 acquired on October 21, 1992, JERS-1 SAR scene 119/265 acquired on October 27, 1992 and aerial photographs taken on October 21, 1992. The study area has been selected in Hanoi City and surrounding area, Vietnam. This is a flat agricultural area with various land use types as water rice, secondary crops like maize, cassava, vegetables cultivation as cucumber, tomato etc. mixed with human settlement and some manufacture facilities as brick and ceramic factories. The use of only optical or microwave data could result in misclassification among some land use features as settlement and vegetables cultivation using frame stages. By combination of multitemporal JERS-1 SAR and TM data these errors have been eliminated so that accuracy of the final land use / land cover map has been improved. The paper describes a methodology for data combination and presents results achieved by the proposed approach.

• Journal of the Korean Wood Science and Technology
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• 제45권2호
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• pp.202-212
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• 2017

This study was to establish the interrelation between chemical compositions and near infrared (NIR) spectra for the classification on distinguishability of domestic gymnosperms. Traditional wet chemistry methods and infrared spectral analyses were performed. In chemical compositions of five softwood species including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cypress (Chamaecyparis obtusa), and cedar (Cryptomeria japonica), their extractives and lignin contents provided the major information for distinction between the wood species. However, depending on the production region and purchasing time of woods, chemical compositions were different even though in same species. Especially, red pine harvested from Naju showed the highest extractive content about 16.3%, whereas that from Donghae showed about 5.0%. These results were expected due to different environmental conditions such as sunshine amount, nutrients and moisture contents, and these phenomena were also observed in other species. As a result of the principal component analysis (PCA) using NIR between five species (total 19 samples), the samples were divided into three groups in the score plot based on principal component (PC) 1 and principal component (PC) 2; group 1) red pine and Korean pine, group 2) larch, and group 3) cypress and cedar. Based on the chemical composition results, it was concluded that extractive content was highly relevant to wood classification by NIR analysis.