• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.159-168
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• 2004

This paper presents hybrid classification method to improve the performance of satellite images classification by combining Bayesian maximum likelihood classifier, ISODATA clustering and fuzzy C-Means algorithm. In this paper, the training data of each class were generated by separating the spectral signature using ISODATA clustering. We can classify according to pixel's membership grade followed by cluster center of fuzzy C-Means algorithm as the mean value of training data for each class. Bayesian maximum likelihood classifier is performed with prior probability by result of fuzzy C-Means classification. The results shows that proposed method could improve performance of classification method and also perform classification with no concern about spectral signature of the training data. The proposed method Is applied to a Landsat TM satellite image for the verifying test.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.12
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• pp.108-115
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• 1994

Most pattern classifiers have been designed based on the ML (Maximum Likelihood) training algorithm which is simple and relatively powerful. The ML training is an efficient algorithm to individually estimate the model parameters of each class under the assumption that all class models in a classifier are statistically independent. That assumption, however, is not valid in many real situations, which degrades the performance of the classifier. In this paper, we propose a minimum-error-rate training algorithm based on the MAP (Maximum a Posteriori) approach. The algorithm regards the normalized outputs of the classifier as estimates of the a posteriori probability, and tries to maximize those estimates. According to Bayes decision theory, the proposed algorithm satisfies the condition of minimum-error-rate classificatin. We apply this algorithm to NPM (Neural Prediction Model) for speech recognition, and derive new disrminative training algorithms. Experimental results on ten Korean digits recognition have shown the reduction of 37.5% of the number of recognition errors.

• Journal of the Korean Society for Nondestructive Testing
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• v.10 no.2
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• pp.23-31
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• 1990

Acoustic Emission(AE) signals obtained during fracture toughness test and fatigue test for nuclear pressure vessel material(SA 508 cl.3) and artificial AE signals from pencil break and ultrasonic pulser were classified using pattern recognition methods. Three different classifiers ; namely Minimum Distance Classifier, Linear Discriminant Classifier and Maximum Likelihood Classifier were used for pattern recognition. In this study, the performance of each classifier was compared. The discrimination of AE signals from cracking and crack surface rubbing was possible and the analysis for crack propagation was applicable by pattern recognition methods.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.33-40
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• 2005

The aim of remotely sensed data classification is to produce the best accuracy map of the earth surface assigning each pixel to its appropriate category of the real-world. The classification of satellite multi-spectral image data has become tool for generating ground cover map. Many classification methods exist. In this study, MLC(Maximum Likelihood Classification), ANN(Artificial neural network), SVM(Support Vector Machine), Naive Bayes classifier algorithms are compared using IKONOS image of the part of Dalsung Gun, Daegu area. Two preprocessing methods are performed-PCA(Principal component analysis), ICA(Independent Component Analysis). Boosting algorithms also performed. By the combination of appropriate feature selection pre-processing and classifier, the best results were obtained.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.2
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• pp.120-126
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• 2009

There are many different types of surface defects on semiconductor Integrated Chips (IC's) caused by various factors during manufacturing process, such as cracks, foreign materials, chip-outs, chips, and voids. These defects must be detected and classified by an inspection system for productivity improvement and effective process control. Among defects, in particular, foreign materials and chips are the most difficult ones to classify accurately. A vision system composed of a carefully designed optical system and a processing algorithm is proposed to detect and classify the defects on QFN(Quad Flat No-leads) packages. The processing algorithm uses features derived from the defect's position and brightness value in the Maximum Likelihood classifier and the optical system is designed to effectively extract the features used in the classifier. In experiments we confirm that this method gives more effective result in classifying foreign materials and chips.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.807-810
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• 2003

This research uses high-resolution satellite images as a source of collecting farmland information. For effectively extract the paddy area, we use texture information and different classify methods to assist the satellite image classification. First, using maximum likelihood classifier to extract paddy information from images. The results show that User Accuracy and Procedure Accuracy of the paddy area can increase from 80.60% to 95.45% and 84.38% to 95.45%. Second, establishing a paddy Knowledge Base and using Knowledge Base Classifier to extract paddy area, and result shows the User Accuracy and Producer Accuracy to be 92.16% and 90.06%. Finally, The result shows we can effectively contribute to the paddy field information extraction from high-resolution satellite images.

• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.253-260
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• 1999

In this paper, we suggest a new direct method for mage segmentation using texture and color information combined through a multivariate linear discriminant algorithm. The color texture is computed in nin 3${\times}$3 masks obtained from each 3${\times}$3${\times}$3 spatio-spectral neighborhood in the image using the classical haralick and Pressman texture features. Among these 9${\times}$28 texture features the best set was extracted from a training set. The resulting set of 10 features were used to segment an image into four different regions. The resulting segmentation was Compared to classical color and texture segmentation methods using both box classifiers and maximum likelihood classification. It compared favourably on the test image from a Fastred-Lightgreen stained prostatic histological tissue section based on visual inspection. The classification accuracy of 97.5% for the new method obtained on the training data was also among the best of the tested methods. If these results hold for a larger set of images, this method should be a useful tool for segmenting images where both color and texture are relevant for the segmentation process.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.9 no.4
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• pp.113-124
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• 1989

In this study, we tried to extract the land-use change information of Seoul city using the multiple date images of the same geographic area. Multiple date image set is MSS('72, '79, '81, '93) and TM('85), and we carried out geometric correction, digitizing(due to the administrative boundary) in pre-processing process. In addition, we performed land-use classification with MLC(Maximum Likelihood Classifier) after improving the predictive accuracy of classification by filtering technique. At the stage of classification, ground truth data, topographic maps, aerial photographs were used to select the training field and statistical data of that time were compared with the classification result to prove the accuracy. As a result, urban area in Seoul has been increased('72 : 25.8 %${\rightarrow}$'81 : 43.0 %${\rightarrow}$'85 : 51.9 %) and Forest area decreased ('72 : 39.0 %${\rightarrow}$'85 : 28.4 %) as we estimated. Finally, it is concluded that the utilzation of satellite imagery is very effective, economical and helpful in the urban land-use/land-cover monitoring.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.58-65
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• 1997

A new automatic classification method with high and stable accuracy for time-series image data is presented in this paper. This method is based on prior condition that a classified map of the target area already exists, or at least one of the time-series image data had been classified. The classified map is used as a reference map to specify training areas of classification categories. The new automatic classification method consists of five steps, i.e., extraction of training data using reference map, detection of changed pixels based upon the homogeneity of training data, clustering of changed pixels, reconstruction of training data, and classification as like maximum likelihood classifier. In order to evaluate the performance of this method qualitatively, four time-series Landsat TM image data were classified by using this method and a conventional method which needs a skilled operator. As a results, we could get classified maps with high reliability and fast throughput, without a skilled operator.