• Journal of Advanced Marine Engineering and Technology
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• v.39 no.9
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• pp.903-910
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• 2015

The purpose of this research is to develop a full-spectral fatigue analysis program, based on rigid-body ship motion analysis, in order to perform a full-spectral fatigue analysis that considers hydroelasticity effects. To gain credibility, fatigue analysis results of two ship types, performed by the developed program, were compared with those of a classification society, and it was found that both are identical. Full-spectral fatigue analysis considering hydroelasticity effects would be developed in further studies by including flexible-body ship motion analysis results and by supplementing the developed program with a wide-band fatigue damage model.

• Phonetics and Speech Sciences
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• v.6 no.2
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• pp.47-54
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• 2014

The purpose of this study was to analyze spectral versus cepstral measurements in esophageal speakers. The comparison between the measurements in thirteen male esophageal speakers was compared with the control group of thirteen normal speakers using the sustained vowel /a/. The main results can be summarized as below: (a) the CPP and L/H ratio of the esophageal group were significantly lower than those of the control group (b) the CPP was significantly correlated with the spectral parameters such as jitter, shimmer, NHR and VTI, and (c) the ROC analysis showed that the threshold of 10.25dB for the CPP achieved a good classification for esophageal speakers, with 100% perfect sensitivity and specificity. Thus, it was known that cepstral-based acoustic measures such as CPP, may be more reliable predictors than other spectral-based acoustic measures such as jitter and shimmer. And it was found that cepstral-based acoustic measures were effective in distinguishing esophageal voice quality from normal voice quality. This research will contribute to establishing a baseline related to speech characteristics in voice rehabilitation with laryngectomees.

• Journal of Forest and Environmental Science
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• v.29 no.2
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• pp.131-137
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• 2013

This study attempts to classify forest species using hyperspectral data for supporting resources management. The primary dataset used was AISA sensor. The sensor was mounted onboard the NOMAD GAF-27 aircraft at 2,000 m altitude creating a 2 m spatial resolution on the ground. Pre-processing was carried out with CALIGEO software, which automatically corrects for both geometric and radiometric distortions of the raw image data. The radiance data set was then converted to at-sensor reflectance derived from the FODIS sensor. Spectral Angle Mapper (SAM) technique was used for image classification. The spectra libraries for tree species were established after confirming the appropriate match between field spectra and pixel spectra. Results showed that the highest spectral signature in NIR range were Kembang Semangkok (Scaphium macropodum), followed by Meranti Sarang Punai (Shorea parvifolia) and Chengal (Neobalanocarpus hemii). Meanwhile, the lowest spectral response were Kasai (Pometia pinnata), Kelat (Eugenia spp.) and Merawan (Hopea beccariana), respectively. The overall accuracy obtained was 79%. Although the accuracy of SAM techniques is below the expectation level, SAM classifier was able to classify tropical tree species. In future it is believe that the most effective way of ground data collection is to use the ground object that has the strongest response to sensor for more significant tree signatures.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.263-270
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• 2009

Noise band removal is a crucial step before spectral matching since the noise bands can distort the typical shape of spectral reflectance, leading to degradation on the matching results. This paper proposes a statistical noise band removal method for hyperspectral data using the correlation coefficient between two bands. The correlation coefficient measures the strength and direction of a linear relationship between two random variables. Considering each band of the hyperspectral data as a random variable, the correlation between two signal bands is high; existence of a noisy band will produce a low correlation due to ill-correlativeness and undirected ness. The unsupervised k-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID in order to evaluate the validation of the proposed method. This paper also proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures. The experimental results conducted on a 228-band hyperspectral data show that while the SAM measure is rather resistant, the performance of SID measure is more sensitive to noise.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.111-116
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• 2021

The hyper-spectral image is data obtained by dividing the electromagnetic wave band in the infrared region into hundreds of wavelengths. It is used to find or classify objects in various fields. Recently, deep learning classification method has been attracting attention. In order to use hyper-spectral image data as deep learning training data, a processing technique is required compared to conventional visible light image data. To solve this problem, we developed a software that selects specific wavelength images from the hyper-spectral data cube and performs the ground truth task. We also developed software to manage data including environmental information. This paper describes the configuration and function of the software.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.419-423
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• 1997

This paper is concerned about the classification of objects together with muti-attributes such as remote sensing image data by using tolerance rough set. To produce more reliable relations from given attributes in the data, we define new similarity measures by using scaling. Our Method will be applied to classify multi-spectral image data.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.125-126
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• 1996

Beijing-Arizona-Taipei-Connecticut (BATC) survey is a long term project to map the spectral energy distribution of various objects using 15 intermediate band filters and aims to cover about 450 sq degrees of northern sky. The SED information, combined with image structure information, is used to classify objects into several stellar and galaxy categories as well as QSO candidates. In this paper, we present a preliminary setup of robust data reduction procedure recently developed at NCU and also briefly discuss general classification scheme: redshift estimate, and automatic detection of variable objects.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.522-524
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• 2012

For exact classification of the defect, good feature selection and classifier is necessary. In this paper, various features such as brightness features, shape features and statistical features are stated and Bayes classifier using Gaussian mixture model is used as classifier. Also feature extraction method based on spectral graph theory is presented. Experimental result shows that feature extraction method using graph Laplacian result in better performance than the result using PCA.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.435-444
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• 2011

The land cover map derived from spectral features of high resolution optical images has low spectral resolution and heterogeneity in the same land cover class. For this reason, despite the same land cover class, the land cover can be classified into various land cover classes especially in vegetation area. In order to overcome these problems, detailed vegetation classification is applied to optical satellite image and SAR(Synthetic Aperture Radar) integrated data in vegetation area which is the result of pre-classification from optical image. The pre-classification and vegetation classification were performed with MLC(Maximum Likelihood Classification) method. The hierarchical land cover classification was proposed from fusion of detailed vegetation classes and non-vegetation classes of pre-classification. We can verify the facts that the proposed method has higher accuracy than not only general SAR data and GLCM(Gray Level Co-occurrence Matrix) texture integrated methods but also hierarchical GLCM integrated method. Especially the proposed method has high accuracy with respect to both vegetation and non-vegetation classification.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.363-371
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• 2015

Feature extraction is one of the main goals in many remote sensing analyses. After high-resolution imagery became more available, it became possible to extract more detailed and specific features. Thus, considerable image segmentation algorithms have been developed, because traditional pixel-based analysis proved insufficient for high-resolution imagery due to its inability to handle the internal variability of complex scenes. However, the individual segmentation method, which simply uses color layers, is limited in its ability to extract various target features with different spectral and shape characteristics. Spectral indices can be used to support effective feature extraction by helping to identify abundant surface materials. This study aims to evaluate a feature extraction method based on a segmentation technique with spectral indices. We tested the extraction of diverse target features-such as buildings, vegetation, water, and shadows from eight band WorldView-2 satellite image using decision tree classification and used the result to draw the appropriate spectral indices for each specific feature extraction. From the results, We identified that spectral band ratios can be applied to distinguish feature classes simply and effectively.