• Spatial Information Research
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• v.12 no.2
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• pp.127-135
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• 2004

The operational availability of multispectral high-resolution satellite imagery, opens up new possibilities for updating forest map. Compared with information acquired by traditional methods (Panchromatic Aerial Photo), these data of for a number of advantages. In this study used 1m spatial resolution and 4 multispectral band, which are capability to update forest map of kind of tree. From the result of this study, First, the visual analysis of the colour composites of the multispectral data made it possible to distinguish some species(conifer, broad-leaved, un-stocked, arable land). Second, forest map and orthorectiffd satellite imagery are not match in the boundary of forest, therefore work have some troubles in the modification of forest map. Third, the distinguish from age-class, girth-class and density are much need experience and skillful about sample such as aerial photo.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.407-414
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• 2014

The aim of the present study was to carry out quantitative analysis of spatial resolution for the influence of the focus size and digital image post-processing on the Computed Radiography (CR). The modulation transfer functions of an edge measuring method (MTF) was used for the evaluation of the spatial resolution. The focus size of X-ray tube was used the small focus (0.6 mm) and the large focus (1.2 mm). We evaluated the 50% and 10% of MTF for the enhancement of edge and contrast by using multi-scale image contrast amplification (MUSICA) in digital image post-processing. As a results, the edge enhancement than the contrast enhancement were significantly higher the spatial resolution of MTF 50% in all focus. Also the spatial resolution of the obtained images in a large focus were improved by digital image processing. In conclusion, the results of this study should serve as a basic data for obtain the high resolution clinical images, such as skeletal and chest images on the CR.

• Atmosphere
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• v.20 no.4
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• pp.495-503
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• 2010

Satellite data have an intrinsic problem due to a number of various physical parameters, which can have a similar effect on measured radiance. Most evaluations of satellite performance have relied on comparisons with limited spatial and temporal resolution of ground-based measurements such as soundings and in-situ measurements. In order to overcome this problem, a new way of satellite data evaluation is suggested with statistical tools such as empirical orthogonal function(EOF), and singular value decomposition(SVD). The EOF analyses with OMI and OMI HCHO over northeast Asia show that the spatial pattern show high correlation with population density. This suggests that human activity is a major source of as well as HCHO over this region. However, this analysis is contradictory to the previous finding with GOME HCHO that biogenic activity is the main driving mechanism(Fu et al., 2007). To verify the source of HCHO over this region, we performed the EOF analyses with vegetation and HCHO distribution. The results showed no coherence in the spatial and temporal pattern between two factors. Rather, the additional SVD analysis between $NO_2$ and HCHO shows consistency in spatial and temporal coherence. This outcome suggests that the anthropogenic emission is the main source of HCHO over the region. We speculate that the previous study appears to be due to low temporal and spatial resolution of GOME measurements or uncertainty in model input data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.499-505
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• 2007

A method of improving the correctness and confidence in land use classification as well as urban spatial structure analysis of local governments using GIS and satellite imagery is suggested. This study also compares and analyzes LSAS (Land Suitability Assessment System) results using two approaches-LSAS with priority classification, and LSAS using standard estimation factors without priority classification. The conclusions that can be drawn from this study are as follows. First, a method of maintaining up-to-date local government data by updating the LSAS database using high-resolution satellite imagery is suggested. Second, to formulate a scientific and reasonable land use plan from the viewpoint of territory development and urban management, a method of simultaneously processing the two described approaches is suggested. Finally, LSAS was constructed by using varieties of land information such as the cadastral map, the digital topographic map, varieties of thematic maps, and official land price data, and expects to utilize urban management plan establishment widely and effectively through regular data updating and problem resolution of data accuracy.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.382-385
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• 2008

MODIS images have a great advantage of high temporal resolution to monitor land cover changes in a large area. The moderate and low spatial resolution satellite images are incomparably economic than high resolution satellite images. As diverse satellite images are provided recently, strategies using satellite images are necessary for continuous, effective and long-term land monitoring. This research purposed to use MODIS images to monitor land cover in Korean peninsula for long-term and continuous change detection. To maximize the advantages of high temporal resolution, the change detection was based on the MODIS temporal profiles of the surface reflectance for one year. In this study as the reflectance patterns of year 2005 were compared with the reflectance patterns of year 2007, the changed pixels could be detected during two years. To set up the threshold value for the decision of change, ASTER images with the higher spatial resolution, 15m, were used for this study. The test area covered the suburban area of metropolitan city, Seoul, where the landcover changes have been frequently happened.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2343-2346
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• 2003

The spatial resolution of multispectral images can be improved by merging them with higher resolution image data. A fundamental problem frequently occurred in existing fusion processes, is the distortion of spectral information. This paper presents a spatially adaptive image fusion algorithm which produces visually natural images and retains the quality of local spectral information as well. High frequency information of the high resolution image to be inserted to the resampled multispectral images is controlled by adaptive gains to incorporate the difference of local spectral characteristics between the high and the low resolution images into the fusion. Each gain is estimated to minimize the l$_2$-norm of the error between the original and the estimated pixel values defined in a spatially adaptive window of which the weight are proportional to the spectral correlation measurements of the corresponding regions. This method is applied to a set of co-registered Landsat7 ETM＋ panchromatic and multispectral image data.

• Korean Journal of Remote Sensing
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• v.24 no.3
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• pp.257-266
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• 2008

This study presents an approach to synthesize multispectral images at a higher resolution by exploiting a high-resolution image acquired in panchromatic modality. The synthesized images should be similar to the multispectral images that would have been observed by the corresponding sensor at the same high resolution. The proposed scheme is designed to reconstruct the multispectral images at the higher resolution with as less color distortion as possible. It uses a regression model of the second order to fit panchromatic data to multispectral observations. Based on the regression model, the multispectral images at the higher spatial resolution of the panchromatic image are optimized by a quadratic programming. In this study, the new method was applied to the IKONOS 1m panchromatic and 4m multispectral data, and the results were compared with them of several current approaches. Experimental results demonstrate that the proposed scheme can achieve significant improvement over other methods.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.489-493
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• 2004

The Modulation Transfer Function (MTF) is commonly used to characterize the spatial quality of imaging systems. This work is the attempt to measure the MTF for KOMPSAT EOC using the non-parametric method as ground inputs. The spatial performance of the KOMPSAT EOC was analyzed by edge method while in flight using multi-temporal image data collected over test site in Seoul. The results from this work demonstrate the potential applicability of this method to estimate MTF for high spatial resolution satellite KOMPSAT-2 that is being developed to be launched in 2005.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.1-6
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• 2007

Two impedance imaging systems of multi-frequency electrical impedance tomography (MFEIT) and magnetic resonance electrical impedance tomography (MREIT) are described. MFEIT utilizes boundary measurements of current-voltage data at multiple frequencies to reconstruct cross-sectional images of a complex conductivity distribution (${\sigma}+i{\omega}{\varepsilon}$) inside the human body. The inverse problem in MFEIT is ill-posed due to the nonlinearity and low sensitivity between the boundary measurement and the complex conductivity. In MFEIT, we therefore focus on time- and frequency-difference imaging with a low spatial resolution and high temporal resolution. Multi-frequency time- and frequency-difference images in the frequency range of 10 Hz to 500 kHz are presented. In MREIT, we use an MRI scanner to measure an internal distribution of induced magnetic flux density subject to an injection current. This internal information enables us to reconstruct cross-sectional images of an internal conductivity distribution with a high spatial resolution. Conductivity image of a postmortem canine brain is presented and it shows a clear contrast between gray and white matters. Clinical applications for imaging the brain, breast, thorax, abdomen, and others are briefly discussed.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.837-839
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• 2003

Low spatial resolution remote sensing (RS) data (LSRD) are promising in agricultural monitoring activities due to their high temporal resolution, but under such a spatial resolution, mixing in a pixel is a common problem. In this study, a numerical experiment was conducted to explore a mixed pixel problem in agriculture using a combined RSsimulation model SWAP (Soil-Water-Atmosphere -Plant) and a Genetic Algorithm (GA) approach. Results of the experiments showed that it is highly possible to address the mixed pixel problem with LSRD.