• Journal of the Korean association of regional geographers
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• v.5 no.1
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• pp.137-150
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• 1999

Five land use change detection methods were applied to 1994 and 1997 Landsat Thematic Mapper (TM) images of Pook-Gu, Taegu city to determine the land-cover changes between the two dates. The two images were coregistred to UTM coordinates. A post-classification comparison method was the most commonly used quantitative method of change detection. A pre-classification comparison method was more effective method to change detection of land cover than a post-classification comparison method. Two indices were used to assess the accuracies of the studied methods. A image differencing method was found to be most accurate for detecting change verse no change among five land use change detection methods. The difference image of band 2 was found to be most accurate. The overall accuracy and Kappa index agreement of the difference image of band 2 were 0.810 and 0.447.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.367-373
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• 2000

Natural and artificial land features are very dynamic, changing somewhat repidly in our lifetime. It is important that such changes are inventoried accurately so that the physical and human processes at work can be more fully understood. Change detection is a technique used to determine the change between two or more time periods of a particular object of study. Change detection is an important process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial distribution in the population of interest. The purpose of this research is to detect environmental changes surrounding an area of Mountain Moscow, Idaho using Landsat Thematic Maper (TM) images of (July 8, 1990 and July 20, 1991). For accurate classification, the Image enhancement process was performed for improving the image quality of each image. A SPOT image (Aug. 14, 1992) was used for image merging in this research. Supervised classification was performed using the maximum likelihood method. Accuracy assessments were done for each classification. Two images were compared on a pixel-by-pixel basis using the post-classification comparison method that is used for detecting the changes of the study area in this research. The 'from-to' change class information can be detected by post classification comparison using this method and we could find which class change to another.

• Journal of the Korean Geographical Society
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• v.45 no.2
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• pp.275-292
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• 2010

This study investigated the land-cover changes in the tidal flat of the Taean peninsula due to man-made environmental changes between 1972 and 2008, through time-series analysis based on a modified post-classification comparison method and multi-temporal satellite images. The analysis revealed that the land-cover of the tidal flat has changed from tidal flat to wetland and from wetland to paddy field between 1972 and 2008. Also, the pattern of detailed land-cover changes is as follows: tidal flat to wetland; lake and saltpan to bare land and paddy field. The accurate classification of each image is needed for the application of the post-classification comparison method. The overall accuracy of the classified images was found to be 95.33% on average, and the Kappa value was 0.941 on average.

• Quality Improvement in Health Care
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• v.10 no.2
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• pp.204-215
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• 2003

Background : ICD-10 Classification, which is used domestically as well as internationally, has limited use in the clinical practice since it is developed for at disease statistics and epidemiology. Therefore, the purposes of this study were to improve the quality of diagnosis by constructing a new disease classification based on the diagnoses doctors currently make in the clinical setting and connecting this classification with OCS and EMR, and to meet the demands of doctors for high quality medical study data in medical research. Methods : The specialists in each ophthalmic subfield collected clinical diagnoses and abbreviations based on the ophthalmology textbooks and confirmed the classifications. Total number of clinical diagnoses collected was totaled 672, for which ideal diagnoses had been selected and a new model of disease classification model in connection with ICD-10 was constructed. The constructed classification of clinical diagnoses consisted of six steps: the first step was the classification by ophthalmic subspecialty field; the second to fifth steps were the detailed classification by each specialty field; the sixth step was the classification by site. Results : After introducing the new disease classification, research on the use and a pre-post comparison was conducted. The result from the research on the use of the clinical diagnoses in inpatient and outpatient care has shown a gradually increasing tendency. From the pre-post comparison of EMR discharge summary diagnoses, the result demonstrated that the diagnosis was stated correctly and in detail. Since the diagnosis was stated correctly, code classification became correct as well, which makes it possible to construct high quality medical DB. Conclusion : This construction of clinical diagnoses provides the medical team with high quality medical information. It is also expected to increase the accuracy and efficiency of service in the department of medical record and department of insurance investigation. In the future, if hospitals wish to construct a classification of clinical diagnosis and a standard proposal of clinical diagnosis is presented by a medical society, the standardization of diagnosis seems to be possible.

• Journal of Korean Society of Forest Science
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• v.81 no.4
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• pp.346-356
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• 1992

The extensive Snake River floodplain in Northwest United States has experienced major changes in water channels and vegetation types due to floodings. To detect the change of wetland cover-types for the period of 1985 and 1988, post-classification comparison and image differencing change detection techniques were evaluated using Landsat-5 TM digital data. Differenced infrared-band images indicated better accuracy indices than any visible-band images. A thresholding technique was applied to identify the change and no change categories from the transformed images produced by image differencing. The problems in using different accuracy indices, including the Kappa coefficient of agreement, overall accuracy, producer's accuracy, user's accuracy, and average accuracy(based on both the producer's and user's accuracy approaches) in determining an optimal threshold level, were examined.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.675-696
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• 2023

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R²) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.315-328
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• 2011

Landsat data incorporated with additional bands-normalized difference vegetation index (NDVI) and band ratios were used to assess the extent and rate of deforestation in the Gunung Mutis Nature Reserve (GMNR), a mountainous tropical forest in Eastern of Indonesia. Hybrid classification was chosen as the classification approach. In this approach, the unsupervised classification-iterative self-organizing data analysis (ISODATA) was used to create signature files and training data set. A statistical separability measurement-transformed divergence (TD) was used to identify the combination of bands that showed the highest distinction between the land cover classes in training data set. Supervised classification-maximum likelihood classification (MLC) was performed using selected bands and the training data set. Post-classification smoothing and accuracy assessment were applied to classified image. Post-classification comparison was used to assess the extent of deforestation, of which the rate of deforestation was calculated by the formula suggested by Food Agriculture Organization (FAO). The results of two periods of deforestation assessment showed that the extent of deforestation during 1989-1999 was 720.72 ha, 0.80% of annual rate of deforestation, and its extent of deforestation during 1999-2009 was 1,059.12 ha, 1.31% of annual rate of deforestation. Such results are important for the GMNR authority to establish strategies, plans and actions for combating deforestation.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.87-98
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• 2010

For generating accurate land cover map over the whole Korean Peninsula, post-mosaic classification method is desirable in large area where multiple image data sets are used. We try to derive an optimal mosaic method of multi-temporal Landsat ETM+ scenes for the land cover classification over the Korea Peninsula. Total 65 Landsat ETM+ scenes were acquired, which were taken in 2000 and 2001. To reduce radiometric difference between adjacent Landsat ETM+ scenes, we apply three relative radiometric correction methods (histogram matching, 1st-regression method referenced center image, and 1st-regression method at each Landsat ETM+ path). After the relative correction, we generated three mosaic images for three seasons of leaf-off, transplanting, leaf-on season. For comparison, three mosaic images were compared by the mean absolute difference and computer classification accuracy. The results show that the mosaic image using 1st-regression method at each path show the best correction results and highest classification accuracy. Additionally, the mosaic image acquired during leaf-on season show the higher radiance variance between adjacent images than other season.

• Journal of Korean Biological Nursing Science
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• v.16 no.4
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• pp.342-348
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• 2014

Purpose: The purpose of this study was to examine the effects of pressure ulcer classification system education on hospital nurses' knowledge and visual discrimination ability of pressure ulcer classification system and incontinence-associated dermatitis. Methods: One group pre- and post-test was used. A convenience sample of 96 nurses participating in pressure ulcer classification system education, were enrolled in single institute. The education program was composed of a 50-minute lecture on pressure ulcer classification system and case-studies. The pressure ulcer classification system and incontinence-associated dermatitis knowledge test and visual discrimination tool, consisting of 21 photographs including clinical information were used. Paired t-test was performed using SPSS/WIN 18.0. Results: The overall mean difference of pressure ulcer classification system knowledge (t=4.67, p<.001) and visual discrimination ability (t=10.58, p<.001) were statistically and significantly increased after pressure ulcer classification system education. Conclusion: Overall understanding of pressure ulcer classification system and incontinence-associated dermatitis after pressure ulcer classification system education was increased, but tended to have lack of visual discrimination ability regarding stage III, suspected deep tissue injury. Differentiated continuing education based on clinical practice is needed to improve knowledge and visual discrimination ability for pressure ulcer classification system, and comparison experiment research is required to evaluate its effects.

• Spatial Information Research
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• v.19 no.6
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• pp.43-54
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• 2011

This study proposes a rule-based image classification method for the time-series analysis of changes in the land surface of the Seongnam-Yongin area using satellite-image data from 2000 to 2009. In order to identify the change patterns during each period, 11 classes were employed in accordance with statistical/mathematic rules. A generalized algorithm was used so that the rules could be applied to the unsupervised-classification method that does not establish any training sites. The results showed that the urban area of the object increased by 145% due to housing-site development. The image data from 2009 had a classification accuracy of 98%. For method verification, the results were compared to land-cover changes through Post-classification comparison. The maximum utilization of the available data within multiple images and the optimized classification allowed for an improvement in the classification accuracy. The proposed rule-based image-classification method is expected to be widely employed for the time-series analysis of images to produce a thematic map for urban development and to monitor urban development and environmental change.