• 한국산림과학회지
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• 제100권4호
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• pp.540-547
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• 2011

본 연구는 고해상도 위성영상인 QuickBird 영상을 이용한 픽셀 및 분할기반의 분류방법의 정확도를 비교하여 적합한 수종 분류방법을 선정하기 위해 수행하였다. 이를 위해 연구대상지인 충청북도 옥천군과 영동군의 산림을 대상으로 현지조사를 실시하여 총 398개 토지피복정보를 수집하였다. 총 14개의 토지 피복 등급(4개의 침엽수종과 7개의 활엽수종, 그리고 3개의 비산림지)으로 구분된 현지조사 자료를 훈련자료로 이용하였다. 픽셀기반 분류에 있어서 위성영상이 가지고 있는 원 화소값, tasseled cap 분석에 의한 3개의 지수, 그리고 주성분 분석을 통한 3개의 성분값을 이용한 3가지의 밴드조합 영상을 생성하여 분류정확도를 비교한 결과, 위성영상의 원 화소값을 이용한 분류 정확도가 가장 높은 것으로 평가되었다. 분할기반 분류에서는 3개의 축척계수에 따른 정확도를 비교한 결과, 축척계수 50%을 적용하였을 때 전체 정확도는 76%, 그리고 ${\hat{k}}$ 값은 0.74로 다른 축척계수에 의한 정확도보다 높은 것으로 나타났다. 결과적으로 QuickBird 영상의 원 화소값과 50%의 축척계수를 이용한 분할기반의 수종분류 결과가 정확도가 가장 높은 것으로 평가되었다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 춘계학술발표회논문집
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• pp.267-273
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• 2004

The advantage of the remote sensing is extraction the information of wide area rapidly. Such advantage is the resource and environment are quick and efficient method to grasps accurately method through the land cover classification of wide area. Accordingly this study is used to the high-resolution (6.6m) Electro-Optical Camera (EOC) panchromatic image of the first Korea Multi-Purpose Satellite 1 (KOMPSAT-1) and the multi-spectral Moderate Resolution Imaging Spectroradiometer (MODIS) image data(36 bands).We accomplished FCM classification technique with MLC technique to be general land cover classification method in the content of research. And evaluated the accuracy assessment of two classification method.

• 대한원격탐사학회지
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• 제22권3호
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• pp.211-219
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• 2006

A random forests classifier is applied to multi-sensor data fusion for supervised land-cover classification in order to account for the importance of variable. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. The distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Two different multi-sensor data sets for supervised classification were used to illustrate the applicability of random forests: one with optical and polarimetric SAR data and the other with multi-temporal Radarsat-l and ENVISAT ASAR data sets. From the experimental results, the random forests approach could extract important variables or bands for land-cover discrimination and showed reasonably good performance in terms of classification accuracy.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.150-150
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• 2019

The development of prolong and severe drought can directly impact on the environment, agriculture, economics and society of country. A lot of efforts have been made across worldwide in the planning, monitoring and mitigation of drought. Currently, different drought indices such as the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) are developed and most commonly used to monitor drought characteristics quantitatively. However, it will be very meaningful and essential to develop a more effective technique for assessment and monitoring of onset and end of drought. Therefore, in this study, the hidden Markov Bayesian classifier (MBC) was employed for the assessment of onset and end of meteorological drought classes. The results showed that the probabilities of different classes based on the MBC were quite suitable and can be employed to estimate onset and end of each class for meteorological droughts. The classification results of MBC were compared with SPI and with past studies which proved that the MBC was able to account accuracy in determining the accurate drought classes. For more performance evaluation of classification results confusion matrix was used to find accuracy and precision in predicting the classes and their results are also appropriate. The overall results indicate that the MBC was effective in predicating the onset and end of drought events and can utilized for monitoring and management of short-term drought risk.

• 한국측량학회지
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• 제25권6_1호
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• pp.499-505
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• 2007

A method of improving the correctness and confidence in land use classification as well as urban spatial structure analysis of local governments using GIS and satellite imagery is suggested. This study also compares and analyzes LSAS (Land Suitability Assessment System) results using two approaches-LSAS with priority classification, and LSAS using standard estimation factors without priority classification. The conclusions that can be drawn from this study are as follows. First, a method of maintaining up-to-date local government data by updating the LSAS database using high-resolution satellite imagery is suggested. Second, to formulate a scientific and reasonable land use plan from the viewpoint of territory development and urban management, a method of simultaneously processing the two described approaches is suggested. Finally, LSAS was constructed by using varieties of land information such as the cadastral map, the digital topographic map, varieties of thematic maps, and official land price data, and expects to utilize urban management plan establishment widely and effectively through regular data updating and problem resolution of data accuracy.

• 보건행정학회지
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• 제25권3호
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• pp.185-196
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• 2015

Background: Issues concerning with the classification accuracy of Korean Outpatient Groups (KOPGs) have been raised by providers and researchers. The KOPG is an outpatient classification system used to measure casemix of outpatient visits and to adjust provider risk in charges by the Health Insurance Review & Assessment Service in managing insurance payments. The objective of this study were to refine KOPGs to improve the classification accuracy and to evaluate the refinement. Methods: We refined the rules used to classify visits with multiple procedures, newly defined chemotherapy drug groups, and modified the medical visit indicators through reviews of other classification systems, data analyses, and consultations with experts. We assessed the improvement by measuring % of variation in case charges reduced by KOPGs and the refined system, Enhanced KOPGs (EKOPGs). We used claims data submitted by providers to the HIRA during the year 2012 in both refinement and evaluation. Results: EKOPGs explicitly allowed additional payments for multiple procedures with exceptions of packaging of routine ancillary services and consolidation of related significant procedures, and discounts ranging from 30% to 70% were defined in additional payments. Thirteen chemotherapy drug KOPGs were added and medical visit indicators were streamlined to include codes for consultation fees for outpatient visits. The % of variance reduction achieved by EKOPGs was 48% for all patients whereas the figure was 40% for KOPGs, and the improvement was larger in data from tertiary and general hospitals than in data from clinics. Conclusion: A significant improvement in the performance of the KOPG was achieved by refining payments for visits with multiple procedures, defining groups for visits with chemotherapy, and revising medical visit indicators.

• 대한원격탐사학회지
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• 제25권3호
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• pp.233-242
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• 2009

This paper presents two different types of supervised classifiers such as support vector machine (SVM) and spectral angle mapper (SAM). The Compact Airborne Spectrographic Imager (CASI) high resolution aerial image was classified with the above two classifier. The image was classified into eight land use /land cover classes. Accuracy assessment and Kappa statistics were estimated for SVM and SAM separately. The overall classification accuracy and Kappa statistics value of the SAM were 69.0% and 0.62 respectively, which were higher than those of SVM (62.5%, 0.54).

• 농업과학연구
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• 제47권3호
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• pp.633-644
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• 2020

Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1^st and 2^nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.16-30
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• 2023

The elderly population is increasing owing to a low fertility rate and an aging population. In addition, life expectancy is increasing, and the advancement of medicine has increased the importance of health to most people. Therefore, government and companies are developing and supporting smart healthcare, which is a health-related product or industry, and providing related services. Moreover, with the development of the Internet, many people are managing their health through online searches. The most convenient way to achieve such management is by consuming nutritional supplements or seasonal foods to prevent a nutrient deficiency. However, before implementing such methods, knowing the nutrient status of the individual is difficult, and even if a test method is developed, the cost of the test will be a burden. To solve this problem, we developed a questionnaire related to nutrient classification twice, based upon which an adaptive algorithm was designed. This algorithm was designed as a machine learning based algorithm for nutrient classification and its accuracy was much better than the other machine learning algorithm.

• 음성과학
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• 제15권3호
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• pp.133-144
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• 2008

The present study examined multicultural family children's articulation accuracy and phonological process using Assessment of Phonology and Articulation for Children(APAC), and compared them with normally developing children. The subjects of this study were 24 multicultural family children between ages 3 years, 6 months and 3 years, 11 months. The multicultural family children's articulation accuracy was significantly lower than the normally developing children's. In case of the normally developing children, phonological processes the multicultural family children showed were observed at a younger age and did not appear at the age of the children participating in this study. The Japanese multicultural family children and the non Japanese multicultural family children showed different rate of the changes. This result shows that articulation development in the multicultural family children may be different among them according to the classification and that the children's error patterns are related to their mothers' native language. The results of this study are proposed to be applicable to articulation assessment and treatment.