• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.207-218
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• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.169-172
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• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have been tried to enhance classification accuracy. Previous studies show that the classification technique based on wavelet transform is more effective than that of traditional techniques based on original pixel values, especially in complicated imagery. Various wavelets can be used in wavelet transform. Wavelets are used as basis functions in representing other functions, like sinusoidal function in Fourier analysis. In these days, some basis functions such as Haar, Daubechies, Coiflets and Symlets are mainly used in 2D image processing. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we computed the wavelet coefficients of satellite image using 10 different basis functions, and then classified test image. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis function. The energy parameter of signal is the sum of the squares of wavelet coefficients. The energy parameter is calculated by sub-bands after the wavelet decomposition and the energy parameter of each sub-band can be a favorable feature of texture. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.4
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• pp.291-297
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• 2013

Recently, the amount of web pages, which include various information, has been drastically increased according to the explosive increase of WWW usage. Therefore, the need for web page classification arose in order to make it easier to access web pages and to make it possible to search the web pages through the grouping. Web page classification means the classification of various web pages that are scattered on the web according to the similarity of documents or the keywords contained in the documents. Web page classification method can be applied to various areas such as web page searching, group searching and e-mail filtering. However, it is impossible to handle the tremendous amount of web pages on the web by using the manual classification. Also, the automatic web page classification has the accuracy problem in that it fails to distinguish the different web pages written in different forms without classification errors. In this paper, we propose the automatic web page classification system using meta-tag that can be obtained from the web pages in order to solve the inaccurate web page retrieval problem.

• Journal of the Korean Geographical Society
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• v.43 no.5
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• pp.761-774
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• 2008

Integration of GIS data and human expert knowledge into digital image processing has long been acknowledged as a necessity to improve remote sensing image analysis. We propose inductive machine learning algorithm for GIS data integration and rule-based classification method for land cover classification. Proposed method is tested with a land cover classification of a Landsat ETM+ multispectral image and GIS data layers including elevation, aspect, slope, distance to water bodies, distance to road network, and population density. Decision trees and production rules for land cover classification are generated by C5.0 inductive machine learning algorithm with 350 stratified random point samples. Production rules are used for land cover classification integrated with unsupervised ISODATA classification. Result shows that GIS data layers such as elevation, distance to water bodies and population density can be effectively integrated for rule-based image classification. Intuitive production rules generated by inductive machine learning are easy to understand. Proposed method demonstrates how various GIS data layers can be integrated with remotely sensed imagery in a framework of knowledge base construction to improve land cover classification.

• Journal of the Korean Society for information Management
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• v.14 no.2
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• pp.105-122
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• 1997

A library for the civil engineering field goes on increasing in quantity because of the growth in construction technology and the enlargement in applicable fields of civil engineering. Most of libraries and information centers in construction companies are using Dewey Decimal Classification (DDC) or Korean Decimal Classification (KDC) to classify a library in civil engineering field. It is necessary for the library classification system to be equipped with a more standardized code system, which corresponds to the academical and technical classification for the civil engineering works. This study analyzes the defects of existing classification systems, and then suggests a new classes and divisions classification system, which facilitates to link academic information with technical data, for the civil engineering field. The proposed system is expected to make practical application of information classification system in the construc ion industry and to be applied for the revised edition of KDC.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.309-326
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• 2008

NTIS(National Science & Technology Information Service) provides the information for domestic research projects. It in particular has several classification schemes to classify research projects and provide better retrieval and analysis services. It however is difficult to understand the characteristic of a research project clearly since only a classification in a classification scheme can be chosen about a research project. Moreover, the classification scheme covers the high-level classification for every research areas so that it cannot cover the area specialized to the oriental medicines. On the other hand, the classification schemes for oriental medicines have recently been studied in oriental medicine field. However, it also covers the high-level classification for oriental medicine so that it may not suit to a classification scheme for research projects. Therefore, in this paper we propose a classification scheme to understand clearly the characteristic of research projects in oriental medicine and use to use them to retrieval and analysis services.

• Safety and Health at Work
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• v.11 no.2
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• pp.152-158
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• 2020

Background: The Globally Harmonized System of Classification and Labeling of Chemicals (GHS) was developed to enhance chemical classification and hazard communication systems worldwide. However, some of the elements such as building blocks and data sources have the potential to cause "disharmony" to the GHS, particularly in its classification results. It is known that some countries have developed their own lists of classified chemicals in accordance with the GHS to "standardize" the classification results within their respective countries. However, the lists of classified chemicals may not be consistent among these countries. Method: In this study, the lists of classified chemicals developed by the European Union, Japan, Malaysia, and New Zealand were selected for comparison of classification results for carcinogenicity, germ cell mutagenicity, and reproductive toxicity. Results: The findings show that only 54%, 66%, and 37% of the classification results for each Carcinogen, Mutagen and Reproductive toxicants hazard classes, respectively are the same among the selected countries. This indicates a "moderate" level of consistency among the classified chemicals lists. Conclusion: By using classification results for the carcinogenicity, germ cell mutagenicity, and reproductive toxicity hazard classes, this study demonstrates the "disharmony" in the classification results among the selected countries. We believe that the findings of this study deserve the attention of the relevant international bodies.

• Journal of Integrative Natural Science
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• v.13 no.4
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• pp.132-140
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• 2020

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.20 no.3
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• pp.219-232
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• 2009

This paper is intended to comparatively analyzed the KDC/NDC/DDC classification system for the field of civil engineering, the research field classification system of National Research Foundation of Korea, and the science and technology research field classification system of Korea Science and Engineering Foundation. And based on the analysis, it tried to propose the ways of improving the KDC classification system for the civil engineering field. As a result of the analysis, this paper has found that the KDC 5th-edition for the civil engineering field needed some corrections. That is, the classification items that reflect the trend of academic development should be added, the classification terminology of the basic theories of civil engineering should be properly developed, segmented topics should be added, any errors in classification codes and Korean/English descriptions should be corrected, and the omission of the KDC relative index of classification items should be solved. This paper proposed the ways of improving those problems.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.57-69
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• 2024

Crop classification plays a vitalrole in monitoring agricultural landscapes and enhancing food production. In this study, we explore the effectiveness of Long Short-Term Memory (LSTM) models for crop classification, focusing on distinguishing between apple and rice crops. The aim wasto overcome the challenges associatedwith finding phenology-based classification thresholds by utilizing LSTM to capture the entire Normalized Difference Vegetation Index (NDVI)trend. Our methodology involvestraining the LSTM model using a reference site and applying it to three separate three test sites. Firstly, we generated 25 NDVI imagesfrom the Sentinel-2A data. Aftersegmenting study areas, we calculated the mean NDVI values for each segment. For the reference area, employed a training approach utilizing the NDVI trend line. This trend line served as the basis for training our crop classification model. Following the training phase, we applied the trained model to three separate test sites. The results demonstrated a high overall accuracy of 0.92 and a kappa coefficient of 0.85 for the reference site. The overall accuracies for the test sites were also favorable, ranging from 0.88 to 0.92, indicating successful classification outcomes. We also found that certain phenological metrics can be less effective in crop classification therefore limitations of relying solely on phenological map thresholds and emphasizes the challenges in detecting phenology in real-time, particularly in the early stages of crops. Our study demonstrates the potential of LSTM models in crop classification tasks, showcasing their ability to capture temporal dependencies and analyze timeseriesremote sensing data.While limitations exist in capturing specific phenological events, the integration of alternative approaches holds promise for enhancing classification accuracy. By leveraging advanced techniques and considering the specific challenges of agricultural landscapes, we can continue to refine crop classification models and support agricultural management practices.