• Korean Journal of Geomatics
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• v.3 no.1
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• pp.59-65
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• 2003

The merging of different scales or multi-sensor image data is becoming a widely used procedure of the complementary nature of various data sets. Ideally, the merging method should not distort the characteristics of the high-spatial and high-spectral resolution data used. To present an effective merging method for survey of reclaimed land using the high-resolution (6.6 m) Electro-Optical Camera (EOC) panchromatic image of the first Korea Multi-Purpose Satellite 1 (KOMPSA T-l) and the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) image data, this paper compares the results of Intensity Hue Saturation (IHS) and Principal Component Analysis (PCA) methods. The comparison is made by statistical and visual evaluation of three-color combination images of IHS and PCA results based on spatial and spectral characteristics. The use of MODIS bands 1, 2, and 3 with a contrast stretched EOC panchromatic image as a substitute for intensity was found to be particularly effective in this study.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.683-691
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• 2012

High resolution satellite images are now widely used for a variety of mapping applications including photogrammetry, GIS data acquisition and visualization. As the spectral and spatial data size of satellite images increases, a greater processing power is needed to process the images. The solution of these problems is parallel systems. Parallel processing techniques have been developed for improving the performance of image processing along with the development of the computational power. However, conventional CPU-based parallel computing is often not good enough for the demand for computational speed to process the images. The GPU is a good candidate to achieve this goal. Recently GPUs are used in the field of highly complex processing including many loop operations such as mathematical transforms, ray tracing. In this study we proposed a technique for parallel processing of high resolution satellite images using GPU. We implemented a spectral radiometric processing algorithm on Landsat-7 ETM+ imagery using CUDA, a parallel computing architecture developed by NVIDIA for GPU. Also performance of the algorithm on GPU and CPU is compared.

• Journal of The Korean Astronomical Society
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• v.44 no.4
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• pp.125-134
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• 2011

We present medium resolution (R = 5000 - 6000) spectra in the near-infrared band, 1.4 - 1.8 ${\mu}m$, for template stars in G, K, and M types observed by the echelle spectrometer, IRCS, at the SUBARU 8.2 m telescope. The identification of lines is based on the spectra of Arcturus (K2 III) in the literature. We measured the equivalent of widths and compared our results to those of Meyer et al. (1998). We conclude that our spectral resolution (R = 6000) data can investigate more accurately the properties of lines in stellar spectra. The library of the template stellar spectra in ASCII format are available for download on the World Wide Web.

• Journal of The Korean Astronomical Society
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• v.37 no.5
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• pp.563-570
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• 2004

Turbulence is a crucial component of dynamics of astrophysical fluids dynamics, including those of ISM, clusters of galaxies and circumstellar regions. Doppler shifted spectral lines provide a unique source of information on turbulent velocities. We discuss Velocity-Channel Analysis (VCA) and its offspring Velocity Coordinate Spectrum (VCS) that are based on the analytical description of the spectral line statistics. Those techniques are well suited for studies of supersonic turbulence. We stress that a great advantage of VCS is that it does not necessary require good spatial resolution. Addressing the studies of mildly supersonic and subsonic turbulence we discuss the criterion that allows to determine whether Velocity Centroids are dominated by density or velocity. We briefly discuss ways of going beyond power spectra by using of higher order correlations as well as genus analysis. We outline the relation between Spectral Correlation Functions and the statistics available through VCA and VCS.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.642-644
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• 2003

SAC is a compact electro-optical camera for imaging in visible-NIR spectral ranges. SAC provides highresolution images over the wide geometric and spectral ranges: 10 m ground sample distance (GSD) and 50 km swath width in the spectral ranges of 520 ${\sim}$ 890 nm. SAC is designed to produce high quality images: modulation transfer function (MTF) of more than 15 %; signal-to-noise ratio (SNR) of more than 100. The missions of SAC incorporate various imaging operations: multi-spectral imaging; super swath-width imaging with cameras in parallel; along-track stereo imaging with slanted 2 cameras.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.341-342
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• 1996

We present here the solar LOG GF values obtained using the Liege solar at las and the standard solar photospheric models for the spectral lines in the wavelength range ${\lambda}{\lambda}$ 6209 - 6273 ${\AA}$. These log gf values shall be used to interpret a high resolution spectra of the star $\gamma$ Draconics.

• Proceedings of the KGS Conference
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• 2004.05a
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• pp.56-56
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• 2004

The ability to detect urban heat islands in satellite imagery is a function of spatial, spectral, and temporal resolutions. Imagery from the satellite-mounted Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor acquired since December 1999 allows us to view the Earth at a higher spectral resolution in the thermal infrared (TIR) portion of the electromagnetic spectrum than most other satellite systems (e.g., AVHRR, Landsat TM). (omitted)

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.75-86
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• 2012

Synthetic Aperture Radar(SAR) imaging system is independent of solar illumination and weather conditions; however, SAR image is difficult to interpret as compared with optical images. It has been increased interest in multi-sensor fusion technique which can improve the interpretability of $SAR^{\circ\circ}$ images by fusing the spectral information from multispectral(MS) image. In this paper, a multi-sensor fusion method based on high-frequency extraction process using Fast Fourier Transform(FFT) and outlier elimination process is proposed, which maintain the spectral content of the original MS image while retaining the spatial detail of the high-resolution SAR image. We used TerraSAR-X which is constructed on the same X-band SAR system as KOMPSAT-5 and KOMPSAT-2 MS image as the test data set to evaluate the proposed method. In order to evaluate the efficiency of the proposed method, the fusion result was compared visually and quantitatively with the result obtained using existing fusion algorithms. The evaluation results showed that the proposed image fusion method achieved successful results in the fusion of SAR and MS image compared with the existing fusion algorithms.

• Journal of the Optical Society of Korea
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• v.19 no.6
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• pp.695-699
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• 2015

A high spectral resolution lidar (HSRL) system based on injection-seeded Nd:YAG laser and iodine absorption filter has been developed for the quantitative measurement of aerosol and cloud. The laser frequency is stabilized at 80 MHz by a frequency locking system and the absorption line of iodine cell is selected at the 1111 line with 2 GHz width. The observations show that the HSRL can provide vertical profiles of particle extinction coefficient, backscattering coefficient and lidar ratio for cloud and aerosol up to 12 km altitude, simultaneously. For the measured cases, the lidar ratios are 10~20 sr for cloud, 28~37 sr for dust, and 58~70 sr for urban pollution aerosol. It reveals the potential of HSRL to distinguish the type of aerosol and cloud. Time series measurements are given and demonstrate that the HSRL has ability to continuously observe the aerosol and cloud for day and night.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.1
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• pp.57-78
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• 2017

There are several statistical classification algorithms available for land use/land cover classification. However, each has a certain bias or compromise. Some methods like the parallel piped approach in supervised classification, cannot classify continuous regions within a feature. On the other hand, while unsupervised classification method takes maximum advantage of spectral variability in an image, the maximally separable clusters in spectral space may not do much for our perception of important classes in a given study area. In this research, the output of an ANN algorithm was compared with the Possibilistic c-Means an improvement of the fuzzy c-Means on both moderate resolutions Landsat8 and a high resolution Formosat 2 images. The Formosat 2 image comes with an 8m spectral resolution on the multispectral data. This multispectral image data was resampled to 10m in order to maintain a uniform ratio of 1:3 against Landsat 8 image. Six classes were chosen for analysis including: Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflected differently in the infrared region with wheat producing the brightest pixel values. Signature collection per class was therefore easily obtained for all classifications. The output of both ANN and FCM, were analyzed separately for accuracy and an error matrix generated to assess the quality and accuracy of the classification algorithms. When you compare the results of the two methods on a per-class-basis, ANN had a crisper output compared to PCM which yielded clusters with pixels especially on the moderate resolution Landsat 8 imagery.