• ETRI Journal
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• v.33 no.4
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• pp.497-505
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• 2011

Image fusion is a technical method to integrate the spatial details of the high-resolution panchromatic (HRP) image and the spectral information of low-resolution multispectral (LRM) images to produce high-resolution multispectral images. The most important point in image fusion is enhancing the spatial details of the HRP image and simultaneously maintaining the spectral information of the LRM images. This implies that the physical characteristics of a satellite sensor should be considered in the fusion process. Also, to fuse massive satellite images, the fusion method should have low computation costs. In this paper, we propose a fast and efficient satellite image fusion method. The proposed method uses the spectral response functions of a satellite sensor; thus, it rationally reflects the physical characteristics of the satellite sensor to the fused image. As a result, the proposed method provides high-quality fused images in terms of spectral and spatial evaluations. The experimental results of IKONOS images indicate that the proposed method outperforms the intensity-hue-saturation and wavelet-based methods.

• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.383-392
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• 2005

Vegetation classes, especially grass and tree classes, are often confused in classification when conventional spectral pattern recognition techniques are used to classify urban areas. This paper reports on a study to improve the classification results by using an automated process of considering height information in separating urban vegetation classes, specifically tree and grass, using three-band, high-spatial resolution, digital aerial imagery. Height information was derived photogrammetrically from stereo pair imagery using cross correlation image matching to estimate differential parallax for vegetation pixels. A threshold value of differential parallax was used to assess whether the original class was correct. The average increase in overall accuracy for three test stereo pairs was $7.8\%$, and detailed examination showed that pixels reclassified as grass improved the overall accuracy more than pixels reclassified as tree. Visual examination and statistical accuracy assessment of four test areas showed improvement in vegetation classification with the increase in accuracy ranging from $3.7\%\;to\;18.1\%$. Vegetation classification can, in fact, be improved by adding height information to the classification procedure.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.103-113
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• 2019

Spatial resolution is still one of the most important parameters for evaluating image quality. In this study, we propose an approach to evaluate spatial resolution and MTF(Modulation Transfer Function) using bar target and Siemens star chart as a part of quality evaluation for UAV images. To this end, images were taken with a fixed-wing eBee(Canon IXUS) at the flight height of 130m and 260m, and with a rotary-wing GD-800(SONY NEX-5N) at flight height of 130m, with a Phantom 4 pro(FC 6310) at flight height of 90m, respectively. Spatial resolution was measured on orthoimages produced from this data. Results show that the resolution measured on the Siemens star and bar target was accurately degraded in proportion to the flight height regardless of the cameras. In the words, the spatial resolution of images taken at the same altitude of 130m with the eBee(Canon IXUS) and the GD-800(SONY NEX-5N) equipped with different cameras was the same as 4.1cm, and that of the eBee(Canon IXUS) at 260m was 8.0cm. In addition, the resolution measured on the Siemens star was about 1~2cm lower than that of the bar target at every flight height. The general tendency was also found to be proportional to the flight height in the measurement of the ${\sigma}_{MTF}$ from MTF, which simultaneously represents the resolution and contrast information of the image. However, at the same altitude of 130m, the ${\sigma}_{MTF}$ of the GD-800(SONY NEX-5N) is 0.36 and the eBee(Canon IXUS) is 0.59, which shows that the GD-800(SONY NEX-5N) has better camera performance. It is expected that study results will contribute to the analysis of spatial resolution of UAV images and to improve the reliability of quality.

• 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 Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.73-81
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• 2010

In recent years, there have been diverse researches and utilizations of creating geo-spatial information with high resolution satellite images. However thematic maps made with middle or low resolution satellite images have low location accuracy and precision of thematic information. This study set out to propose a method of making a precision thematic map with high resolution satellite images by examining the conversion from the conventional method based on middle or low resolution satellite images to the automatic method based on high resolution satellite images of GSD 1m or lower, extracting thematic information of middle or large scale of 1/5,000 or lower, and analyzing its accuracy. Seven classification classes were categorized according to the object-oriented classification in order to automatically extract thematic information with high resolution satellite images. And the classification results were compared and analyzed with the old middle scale land cover map and 1/1000 digital map.

• The Plant Pathology Journal
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• v.26 no.1
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• pp.37-48
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• 2010

This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of $240\;m{\times}240\;m$ based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast, sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.145-154
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• 2009

High spatial resolution satellite image classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, the extraction of the spatial information is one of the most important steps in high resolution satellite image classification. This study proposes a new spatial feature extraction method, named SSI(Shape-Size Index). SSI uses a simple region-growing based image segmentation and allocates spatial property value in each segment. The extracted feature is integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a SVM(Support Vector Machines) classifier. In order to evaluate the proposed feature extraction method, KOMPSAT-2 and QuickBird-2 data are used for experiments. It is demonstrated that proposed SSI algorithm leads to a notable increase in classification accuracy.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.603-606
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• 2002

EZW, a wavelet transformation based compression algorithm, utilizes the correlations of the wavelet coefficients between subbands. Due to the reason that, it is based on Zero Tree's parent-descendant dependency of the subbands, subband coding is possible, but it is not efficient for applications, which require multi-resolution restorations. In this paper we compare and analyze the performance between spatial band coding, which apply the correlation between adjusting pixels, and EZW So as to prove that spatial band coding is superior to EZW in the sense that it exhibit higher PSNR values and that it is apt to molti-resolution restoration.

• Transactions of the Society of Information Storage Systems
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• v.5 no.1
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• pp.36-40
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• 2009

We describe the experimental realization of image system using wavefront coding in 3-Mega pixel mobile phone camera. We designed aspheric lens to extend the depth of field (DOF) using wavefront coding. In addition, through the aspheric lens and lens barrel manufacturing, we obtained a raw image from a camera module. In our method, the acquired images are restored in the spatial frequency domain using the proposed filter and the spatial frequency response (SFR) is calculated. The proposed filters are composed of image denoising filter using low band pass filter in frequency domain and restoration filter for image restoration. Finally, we achieve an enhanced image by super-resolution image processing. Visual examples are given to demonstrate the performance of the proposed filter.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.272-289
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• 2004

The spatial resolution of local weather and climate information for agronomic practices exceeds the current weather service scale. To supplement the insufficient spatial resolution of official forecasts and observations, gridded climate data are frequently generated. Most ecological models can be run using gridded climate data to produce ecosystem responses at landscape scales. In this lecture, state of the art techniques derived from geospatial climatology, which can generate gridded climate data by spatially interpolating point observations at synoptic weather stations, will be introduced. Removal of the urban effects embedded in the interpolated surfaces of daily minimum temperature, incorporation of local geographic potential for cold air accumulation into the minimum temperature interpolation scheme, and solar irradiance correction for daytime hourly temperature estimation are presented. Some experiences obtained from their application to real landscapes will be described.