• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.23-33
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• 2000

On this study, Among the classification methods of land cover using satellite imagery, we compared the classification accuracy of Neural Network Classifier and that of Maximum Likelihood Classifier which has the characteristics of parametric and non-parametric classification method. In the assessment of classification accuracy, we analyzed the classification accuracy about testing area as well as training area that many analysts use generally when assess the classification accuracy. As a result, Neural Network Classifier is superior to Maximum Likelihood Classifier as much as 3% in the classification of training data. When ground reference data is used, we could get poor result from both of classification methods, but we could reach conclusion that the classification result of Neural Network Classifier is superior to the classification result of Maximum Likelihood Classifier as much as 10%.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.521-528
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• 2007

The overall objective of this research was to investigate various combination of segmentation parameters and to improve classification accuracy of object-oriented classification. This research presents a method for evaluation of segmentation parameters by calculating Moran's I and Intrasegment Variance. This research used Landsat-7/ETM image of $11{\times}14$ Km developed area in Ansung, Korea. Segmented images are generated by 75 combinations of parameter. Selecting 7 combinations of high, middle and low grade expected classification accuracy was based on calculated Moran's I and Intrasegment Variance. Selected segmentation images are classified 4 classes and analyzed classification accuracy according to method of objected-oriented classification. The research result proved that classification accuracy is related to segmentation parameters. The case of high grade of expected classification accuracy showed more than 85% overall accuracy. On the other hand, low ado showed around 50% overall accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.265-267
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• 2001

본 논문에서는 레이블이 없는 데이터를 이용하는 새로운 자동 문서 분류 방법을 제시한다. 제시된 방법은 적은 수의 레이블이 있는 데이터로부터 학습된 후 많은 수의 레이블이 없는 데이터로 보강되는 일련의 분류기(classifier)에 기반한다. 레이블이 없는 데이터를 활용하기 때문에, 필요한 레이블이 있는 데이터의 수가 줄어들고, 분류 정확도가 향상된다. 두 개의 표준 데이터 집합에 대한 실험 결과, 레이블이 없는 데이터를 사용함으로써 분류 정확도가 증가함을 보였다. 분류 정확도는 전체 데이터의 2/3만 사용하고도 NIPS 2000 워크숍 데이터 집합에 대해서는 약 7.9% 정도, WebKB 데이터 집합에 대해서는 9.2% 증가하였다.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.47-60
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• 2000

On this study, Among the classification methods of land cover using satellite imagery, we compared the classification accuracy of Neural Network Classifier and that of Maximum Likelihood Classifier which has the characteristics of parametric and non-parametric classification method. In the assessment of classification accuracy, we analyzed the classification accuracy about testing area as well as training area that many analysts use generally when assess the classification accuracy. As a result, Neural Network Classifier is superior to Maximum Likelihood Classifier as much as 3% in the classification of training data. When ground reference data is used, we could get poor result from both of classification methods, but we could reach conclusion that the classification result of Neural Network Classifier is superior to the classification result of Maximum Likelihood Classifier as much as 10%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.248-251
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• 2013

컴퓨터 비전에서 BoW를 이용한 장면 분류 기법에 대한 연구가 활발히 진행되고 있다. BoW 기법의 장면 분류는 K-means 클러스터링을 통하여 코드북을 생성하는 과정에서 트레이닝 이미지의 클래스 정보를 활용하지 않기 때문에 성능이 제한적이라는 문제점을 가지고 있다. 본 논문에서는 BoW를 이용한 장면 분류 과정에서 코드북 생성을 위하여 각각 특징 기술자들의 유클리디안 거리뿐만이 아니라 클래스 확률 밀도 함수들의 히스토그램 교차값을 최소화 하는 최적화 K-means 클러스터링 기법을 제안한다. 장면의 SIFT 특징 기술자 정보뿐만 아니라 장면이 속해있는 클래스 정보를 결합하여 클러스터링을 수행함으로써 장면 분류의 정확도를 높일 수 있다. 장면 분류 정확도 실험에서 제안하는 클러스터링을 사용한 BoW 장면 분류 기법은 기존의 K-means을 사용한 BoW 장면 분류 기법보다 높은 정확도를 보여준다.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.795-801
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• 2013

Recently, support vector machine has been widely used in various application fields due to its superiority of classification performance comparing with decision tree and neural network. Since support vector machine is basically designed for the binary classification problem, output coding method to analyze the classification result of multiclass binary classifier is used for the application of support vector machine into the multiclass problem. However, previous feature selection method for output coding based support vector machine found the features to improve the overall classification accuracy instead of improving each classification accuracy of each classifier. In this paper, we propose the novel feature selection method to find the features for maximizing the classification accuracy of each binary classifier in output coding based support vector machine. Experimental result showed that proposed method significantly improved the classification accuracy comparing with previous feature selection method.

• Annual Conference on Human and Language Technology
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• 2009.10a
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• pp.35-39
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• 2009

이메일의 사용증가로 수신 메일을 효율적이면서 정확하게 분류할 필요성이 점차 증가하고 있다. 현재의 이메일 분류는 베이지안, 규칙 기반 등을 이용하여 스팸 메일을 필터링하기 위한 이원 분류가 주를 이루고 있다. 클러스터링을 이용한 다원 분류 방법은 분류의 정확도가 떨어지는 단점이 있다. 본 논문에서는 비음수 행렬 분해(NMF, Non-negative Matrix Factrazation)를 기반으로 한 자동 분류 주제 생성 방법과 동적 분류 체계(DCH, Dynamic Category Hierachy) 방법을 결합한 새로운 이메일 분류 방법을 제안한다. 이 방법은 수신되는 이메일을 자동으로 분류하여 대량의 메일을 효율적으로 관리할 수 있으며, 분류 결과 사용자의 요구사항을 만족하지 못하면 메일을 동적으로 재분류 하여 분류 정확률을 높일 수 있다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.569-577
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• 2008

The purpose of this study is focused on the development of compound classification process by mixing multitemporal data and annexing a specific image enhancement technique with a specific image classification algorithm, to gain more accurate land information from satellite imagery. That is, this study suggests the classification process using canonical correlation classification technique after principal component analysis for the mixed multitemporal data. The result of this proposed classification process is compared with the canonical correlation classification result of one date images, multitemporal imagery and a mixed image after principal component analysis for one date images. The satellite images which are used are the Landsat 5 TM images acquired on July 26, 1994 and September 1, 1996. Ground truth data for accuracy assessment is obtained from topographic map and aerial photograph, and all of the study area is used for accuracy assessment. The proposed compound classification process showed superior efficiency to appling canonical correlation classification technique for only one date image in classification accuracy by 8.2%. Especially, it was valid in classifying mixed urban area correctly. Conclusively, to improve the classification accuracy when extracting land cover information using Landsat TM image, appling canonical correlation classification technique after principal component analysis for multitemporal imagery is very useful.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2751-2756
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• 2012

Selection of training set influences the classification accuracy in supervised classification using satellite imagery. Generally, if pure pixels which character of training set is clear were selected, whole accuracy is high while if mixed pixels were selected, accuracy is decreased because of low-resolution imagery or unclear distinguishment. However, it is too difficult to choose the pure pixels as training set actually. Accordingly, this study should be suggested the suitable classification method in case of mixed pixels choice. To achieve this, a few pure pixels were chosen as training set and classification accuracy was calculated which was compared with classification result using an equal number of mixed pixels. As a result, accuracy of SVM was the highest among the classification method using mixed pixels and it was a relatively small difference with the result of classification using pure pixels. Therefore, imagery classification using SVM is most suitable in the mixed area of construction and green because it is high possibility to choose mixed pixels as training set.

• Journal of Korean Society for Geospatial Information Science
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• v.9 no.2 s.18
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• pp.37-46
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• 2001

Spectral pattern recognition techniques are most used in classification of remotely sensed data. Yet, in any real image, adjacent pixels are related, because imaging sensors acquire significant portions of energy from adjacent pixels. And, with the continued improvement in the spatial resolution of remote sensing systems, another spatial pattern recognition approach is must considered. In this study, we aim to show the potentiality of spatial classification methods through comparing the accuracies of spectral classification methods and those of spectral classification methods. By the comparisons between the two methods, classification accuracies of 6 different spatial classification methods are higher than that of spectral classification method by 2-6% or so. Additionally, we can show it statistically through the classification experiments with different band combinations.