• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.625-628
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• 2010

This paper proposes a novel feature extraction method for unsupervised multispectral image segmentation based in one dimensional combined neighborhood differences (1D CND). In contrast with the original CND, which is applied with traditional image, 1D CND is computed on a single pixel with various bands. The proposed algorithm utilizes the sign of differences between bands of the pixel. The difference values are thresholded to form a binary codeword. A binomial factor is assigned to these codeword to form another unique value. These values are then grouped to construct the 1D CND feature image where is used in the unsupervised image segmentation. Various experiments using two LANDSAT multispectral images have been performed to evaluate the segmentation and classification accuracy of the proposed method. The result shows that 1D CND feature outperforms the spectral feature, with average classification accuracy of 87.55% whereas that of spectral feature is 55.81%.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.187-192
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• 2006

Image fusion techniques are widely used to integrate a lower spatial resolution multispectral image with a higher spatial resolution panchromatic image. However, the existing techniques either cannot avoid distorting the image spectral properties or involve complicated and time-consuming decomposition and reconstruction processing in the case of wavelet transform-based fusion. In this study a simple spectral preserve fusion technique: the Smoothing Filter-based Replacement(SFR) is proposed based on a simplified solar radiation and land surface reflection model. By using a ratio between a higher resolution image and its low pass filtered (with a smoothing filter) image, spatial details can be injected to a co-registered lower resolution multispectral image minimizing its spectral properties and contrast. The technique can be applied to improve spatial resolution for either colour composites or individual bands. The fidelity to spectral property and the spatial quality of SFM are convincingly demonstrated by an image fusion experiment using IKONOS panchromatic and multispectral images. The visual evaluation and statistical analysis compared with other image fusion techniques confirmed that SFR is a better fusion technique for preserving spectral information.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.5
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• pp.628-636
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• 1999

This work describes the low-resolution spectral modeling of the water vapor, carbon dioxide and their mixtures by applying the weighted-sum-of-gray-gas-gases model (WSGGM) to each narrow band. Proper modeling scheme of gray gas absorption coefficients vs temperature relation is suggested. Comparison between the modeled emissivity calculated from this relation and the 'true' emissivity obtained from the high temperature statistical narrow band parameters is made for a few typical narrow bands. Low resolution spectral intensities from one-dimensional layers are also obtained and examined for uniform, parabolic and boundary layer type temperature profiles using the obtained WSGGM's with several gray gases. The results are compared with the narrow band spectral intensities obtained by a narrow band model-based code with Curtis-Godson approximation. Good agreement is found between them. Data bases including optimized modeling parameters and total and low-resolution spectral weighting factors are developed for water vapor, carbon dioxide and their mixtures. This model and obtained data bases, available from the authors' Internet site, can be appropriately applied to any radiative transfer equation solver.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.143-152
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• 2004

Researchers seeking geological and environmental information, depend on remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, adverse weather conditions as well as equipment expense limit the ability to collect data anywhere and anytime. To allow for better flexibility in geological and environmental data collection, we have developed a compact, multi-spectral automatic Aerial Photographic system (PKNU2). This system's Multi-spectral camera can record visible (RGB) and infrared (NIR) band (3032＊2008 Pixels) images Visible and infrared band images were obtained from each camera respectively and produced color-infrared composite images to be analyzed for the purpose of the environmental monitoring. However this did not provide quality data. Furthermore, it has the disadvantage of having the stereoscopic overlap area being 60% unsatisfied due to the 12 seconds of storage time of each data The PKNU2 system in contrast, photographed photos of great capacity Thus, with such results, we have been proceeding to develop the advanced PKNU2 (PKNU3) system that consists of a color-infrared spectral camera that can photograph in the visible and near-infrared bands simultaneously using a single sensor, a thermal infrared camera, two 40G computers to store images, and an MPEG board that can compress and transfer data to the computer in real time as well as be able to be mounted onto a helicopter platform.

• Journal of the Optical Society of Korea
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• v.20 no.6
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• pp.752-761
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• 2016

Hyperspectral images are often contaminated with stripe noise, which severely degrades the imaging quality and the precision of the subsequent processing. In this paper, a variational model is proposed by employing spectral-spatial adaptive unidirectional variation and a sparse representation. Unlike traditional methods, we exploit the spectral correction and remove stripes in different bands and different regions adaptively, instead of selecting parameters band by band. The regularization strength adapts to the spectrally varying stripe intensities and the spatially varying texture information. Spectral correlation is exploited via dictionary learning in the sparse representation framework to prevent spectral distortion. Moreover, the minimization problem, which contains two unsmooth and inseparable $l_1$-norm terms, is optimized by the split Bregman approach. Experimental results, on datasets from several imaging systems, demonstrate that the proposed method can remove stripe noise effectively and adaptively, as well as preserve original detail information.

• Journal of Pharmaceutical Investigation
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• v.37 no.6
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• pp.377-395
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• 2007

Near infrared(NIR) spectral data have been used for the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. It is very difficult to select the proper wavelengths of spectral data, which give the best PCR(principal component regression) models for the analysis of constituents of biological samples. The NIR data were used after polynomial smoothing and differentiation of 1st order, using Savitzky-Golay filters. To find the best PCR models, all-possible combinations of available principal components from the given NIR spectral data were derived by in-house programs written in MATLAB codes. All of the extensively generated PCR models were compared in terms of SEC(standard error of calibration), $R^2$, SEP(standard error of prediction) and SECP(standard error of calibration and prediction) to find the best combination of principal components of the initial PCR models. The initial PCR models were found by SEC or Malinowski's indicator function and a priori selection of spectral points were examined in terms of correlation coefficients between NIR data at each wavelength and corresponding concentrations. For the test of the developed program, aqueous solutions of BSA(bovine serum albumin) and glucose were prepared and analyzed. As a result, the best PCR models were found using a priori selection of spectral points and the final model selection by SEP or SECP.

• The Korean Journal of Pharmacology
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• v.32 no.3
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• pp.293-300
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• 1996

During the development of hepatic encephalopathy after thioacetamide (TAA) injection to rat, EEG was recorded at two different states: without or with tactile stimulation of tail at regular intervals. Calculations based on the spectral and band analysis were used. The changes in the power spectra and bands were examined in 3 different behavioral stages: normal, mild ataxia and severe ataxia. In normal rats, the stimulation produced the increase in the power of the theta $(3.5{\sim}8\;Hz)$ and the gamma $(30{\sim}50\;Hz)$ bands. These changes could not be produced in rats with the mild and severe ataxia. The changes in the power of the theta band occurred earlier than those of the beta3 and the gamma bands in the stimulated state. Gradual decreases in the spectral power of the beta3 $(21{\sim}30\;Hz)$ and the gamma bands were correlated with the progress of the stages from normal condition to mild to severe ataxia in both unstimulated and stimulated states. The results indicate that the spectral and band analysis used in this study can quantify the severity of the neurological malfunction during HE.

• Phonetics and Speech Sciences
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• v.4 no.3
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• pp.119-125
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• 2012

This paper describes a new algorithm for encoding spectral shape and envelope in the generic mode of G.718 super-wide band (SWB). In the G.718 SWB coder, generic mode coding and sinusoidal enhancement are used for the quantization of modified discrete cosine transform (MDCT)-based parameters in the high frequency band. In the generic mode, the high frequency band is divided into sub-bands and for every sub-band the most similar match with the selected similarity criteria is searched from the coded and envelope normalized wideband content. In order to improve the quantization scheme in high frequency region of speech/audio signals, the modified generic mode by the improvement of the generic mode in G.718 SWB is proposed. In the proposed generic mode, perceptual vector quantization of spectral envelopes and the resolution increase for spectral copy are used. The performance of the proposed algorithm is evaluated in terms of objective quality. Experimental results show that the proposed algorithm increases the quality of sounds significantly.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.37-42
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• 1999

A compact imaging system, the Multi-spectral Earth Imaging System (MEIS) was developed and operated on an engineering test satellite, KITSAT-3 at the orbital altitude of 720 km. The MEIS takes multi-spectral images of the earth's surface with the swath width of 48 km and the ground sampling distance of 13.8 m in three spectral bands. A brief technical description of the KITSAT-3 MEIS and the result from its initial operation since early June, 1999 are presented. The quality of images produced by the KITSAT-3 MEIS was found comparable to that of images from existing commercial earth observation satellites from its preliminary assessment.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.2
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• pp.328-335
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• 2010

A hyperspectral imaging spectrometer has shown significant advantages in performance over other existing ones for remote sensing applications. It can collect hundreds of narrow, adjacent spectral bands for each image, which provides a wealth of information on unique spectral characteristics of objects. We have developed a compact hyperspectral imaging system that successively shows high spatial and spectral resolutions and fast data processing performance. In this paper, we present an overview of the hyperspectral imaging system including the strucure of geometrical optics and several image processing schemes such as wavelength calibration and noise reduction for image data on Visible and Near-Infrared(VNIR) and Shortwave-Infrared(SWIR) band.