• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.776-782
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• 2003

This paper develops an approach to fuse 1-meter resolution spatial panchromatic and 4-meter resolution multi-spectral IKONOS images. The approach is based on the characteristics of four-band wavelet transformation. The experiment shows that the fused images based on four-band wavelet method contain with not only high spatial resolution but also rich spectral characteristic.

• 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.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.395-400
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• 1994

High resolution spectral analysis is essential for ECG anaysis. The fast Fourier transform has been widely used for frequency analysis of ECG signals but this procedure provides poor resolution when the data record is short and shows Gibb's phenomena. The ARMA FTF (Fast Transversal Filter) algorithm is used for high resolution spectral analysis. The reason of unsalability of this algorithm is investigated and the method for improving the numerical stability is proposed. The proposed algorithm is applied to spectral analysis of the ECG. Since this result has less variations than the FFT based results, it can be used for the computerized diagonosis of the ECG.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.137-139
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• 2008

This study presents an approach to reconstruct high-resolution imagery for multispectral imagery of low-resolution using panchromatic imagery of high-resolution. The proposed scheme reconstructs a high-resolution image which agrees with original spectral values. It uses a linear model of high-and low- resolution images and consists of two stages. In this study, an 1m RGB image was generated from 4m IKONOS multispectral data.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.7-14
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• 2006

Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.161-166
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• 2003

Hyperspectral imagery have high spectral resolution and provide the potential for more accurate and detailed information extraction than any other type of remotely sensed data. In this paper, the "Linear Spectral Unmixing" model which is one solution to overcome the limit of spatial resolution for remote sensing data was introduced and we applied the algorithm to hyperspectral image. The result was not good because of some problems such as image calibration and used endmembers. Therefore, we analyzed the cause and had a search for a solution.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.283-285
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• 2003

This paper describes a data fusion method for high spatial resolution satellite imagery. The pixels located around an object edge have spectral mixing because of the geometric primitive of pixel. The larger a size of pixel is, the wider an area of spectral mixing is. The intensity of pixels adjacent edges were modified by the spectral characteristics of the pixels located inside of objects. The methods developed in this study were tested using IKONOS Multispectral and Pan data of a part of Jeju-shi in Korea. The test application shows that the spectral information of the pixels adjacent edges were improved well.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.41-47
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• 2006

Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.213-216
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• 2005

A novel hyperspectral imaging spectrometer controlling spatial and spectral resolution individually has been proposed. This imaging spectrometer uses a zoom lens as a telescope and a focusing element. It can change the spatial resolution fixing the spectral resolution or the spectral resolution fixing the spatial resolution. Here, we report the concept of the hyperspectral imaging spectrometer with the novel zooming function and the optical design of a zoom lens as the focusing element. By using lens module and third-order aberration theory, we have presented the initial design of four-group zoom lens with external entrance pupil. And the optimized zoom lens with a focal length of 50 to 150 mm is obtained from the initial design by the optical design software. As a result, the designed zoom lens shows satisfactory performances in wavelength range of 450 to 900 nm as a focusing element in an imaging spectrometer. Furthermore, the collimator lens of the imaging spectrometer is designed through the third-order aberration correction by using an iterative process.

• Proceedings of the KSRS Conference
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• v.2
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• pp.647-650
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• 2006

Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery was acquired over the Little Miami River Watershed (1756 square miles) in Ohio, U.S.A., which is one of the largest hyperspectral image acquisition. For the development of a 4m-resolution land cover dataset, a hierarchical approach was employed using two different classification algorithms: 'Image Object Segmentation' for level-1 and 'Spectral Angle Mapper' for level-2. This classification scheme was developed to overcome the spectral inseparability of urban and rural features and to deal with radiometric distortions due to cross-track illumination. The land cover class members were lentic, lotic, forest, corn, soybean, wheat, dry herbaceous, grass, urban barren, rural barren, urban/built, and unclassified. The final phase of processing was completed after an extensive Quality Assurance and Quality Control (QA/QC) phase. With respect to the eleven land cover class members, the overall accuracy with a total of 902 reference points was 83.9% at 4m resolution. The dataset is available for public research, and applications of this product will represent an improvement over more commonly utilized data of coarser spatial resolution such as National Land Cover Data (NLCD).