• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.4
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• pp.37-42
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• 2009

This study extracted Near Infrared (NIR) band using Image Processing Technology (IPT), and calculated Normalized Difference Vegetation Index (NDVI). Aerial photography from Daum portal in combination with high resolution satellite image was employed to improve vegetation sensitivity by extracting NIR band and calculating NDVI with comparison to QuickBird result. The extracted NIR band and NDVI through IPT presented similar distribution pattern. In addition, a regression analysis by land cover character showed high correlation paddy and forest Therefore, this approach could be acceptable to acquire vegetation environment information.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.31-39
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• 2007

In this study, the hydrosphere change of the Daecheong dam basin was detected qualitatively and quantitatively using Landsat satellite images until recentness since the construction of Daecheong dam. The hydrosphere change of the basin was analyzed by applying supervised classification about Landsat satellite images which were classified according to the hydrosphere, vegetation, road and etc. for four distinct years which are 1981, 1987, 1993, and 2002 year. Landsat satellite images of each year were achieved overlay analysis with extracting only the hydrosphere, and though these results, the hydrosphere change of the Daecheong dam basin was monitored efficiently.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.427-435
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• 2000

It is often crucial to obtain a map of flood inundated area with more accurate and rapid manner. This study attempts to evaluate the potential of satellite synthetic aperture radar (SAR) data for mapping of flood inundated area in Imjin river basin. Multitemporal RADARSAT SAR data of three different dates were obtained at the time of flooding on August 4 and before and after the flooding. Once the data sets were geometrically corrected and preprocessed, the temporal characteristics of relative radar backscattering were analyzed. By comparing the radar backscattering of several surface features, it was clear that the flooded rice paddy showed the distinctive temporal pattern of radar response. Flooded rice paddy showed significantly lower radar signal while the normally growing rice paddy show high radar returns, which also could be easily interpreted from the color composite imagery. In addition to delineating the flooded rice fields, the multitemporal radar imagery also allow us to distinguish the afterward condition of once-flooded rice field.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1085-1095
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• 2023

Time-series data generated from satellite data are crucial resources for change detection and monitoring across various fields. Existing research in time-series data generation primarily relies on single-image analysis to maintain data uniformity, with ongoing efforts to enhance spatial and temporal resolutions by utilizing diverse image sources. Despite the emphasized significance of time-series data, there is a notable absence of automated data collection and preprocessing for research purposes. In this paper, to address this limitation, we propose a system that automates the collection of satellite information in user-specified areas to generate time-series data. This research aims to collect data from various satellite sources in a specific region and convert them into time-series data, developing an automatic satellite image collection system for this purpose. By utilizing this system, users can collect and extract data for their specific regions of interest, making the data immediately usable. Experimental results have shown the feasibility of automatically acquiring freely available Landsat and Sentinel images from the web and incorporating manually inputted high-resolution satellite images. Comparisons between automatically collected and edited images based on high-resolution satellite data demonstrated minimal discrepancies, with no significant errors in the generated output.

• Korean Journal of Environment and Ecology
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• v.24 no.5
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• pp.575-581
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• 2010

This study was accomplished for verifying the biotope field survey accuracy in the forests. Biotope data is used as the standard for the preservation and restoration of the urban ecosystem. The study area is the forests of Gwanak-gu, Seoul. For verifying accuracy, first we compared biotope field survey results between 2000 and 2005, second we compared between field survey results and satellite imagery. For comparing with satellite imagery, we delineated the evergreen-coniferous forests from imagery taken during winter season. As a result of comparison, the ratio of most actual vegetation types by delineated detail field surveys were matched around 92% between 2000 and 2005. But, between 2 field surveys, around 60% of total vegetation type was regionally matched. Evergreen-coniferous forests extracted by satellite imagery were regionally matched 69.4% of field survey result in 2000, and matched 80% of the result in 2005. If we consider the delineating errors from deciphering the picture, the results have high accuracy, especially 2005. The processes of verifying accuracy have not been proceeding in the part of delineating actual vegetation works. The verification of accuracy is important for the renewal process. Thus, the various verification methods will be studied and criteria should be developed for enhancing objectivity.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.237-240
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• 2004

In this study, the digital image processing with image enhancement based on homomorphic filtering was performed using geophysical imaging data such as gravity, magnetic data and sub-scenes of satellite images such as LANDSAT, IKONOS, and KOMPSAT. Windows application program for executing homomorphic filtering was designed and newly implemented. In general, homomorphic filtering is technique that is based on Fourier transform, which enhances the contrast of image by removing the low frequencies and amplifying the high frequencies in frequency domain. We can enhance the image selectively using homomorphic filtering as compared with the existing method, which enhance the image totally. Through several experiment using remotely sensed imagery and geophysical image with this program, it is concluded that homomorphic filtering is more effective to reveal distinct characteristics for some complicated and multi-associated features on image data.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.371-374
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• 2008

In very high-spatial resolution remote sensing imagery, it is difficult to extract the feature information of various objects because of occlusion and shadows. Moreover, various and feeble information within shadows can be of use in GIS-based applications and remote sensing analysis. In this paper, we developed a radiometric restoration method for shadow areas using KOMPSAT-2 satellite image. After detecting the shadow, non-shadow pixels nearby are extracted using a morphological filter. An iterative linear regression method is applied to calculate the relationship between shadow and non-shadow pixels. The shadows are restored by the parameters of the linear regression algorithm. Tests show that recovery of shadowed areas by our method leads to improved image quality.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.53-56
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• 2008

Generally, the quality of satellite images is expressed by GSD (Ground Sample Distance), MTF (Modulation Transfer Function) and SNR (Signal to Noise Ratio). However, these factors are technology-oriented and do not explain interpretability of satellite images. We need a standardized index which shows standard of interpretability. In this study, we estimated NIIRS (National Imagery Interpretability Rating Scale) through the GIQE (General Image Quality Equation) which is able to judge image interpretability with the standardized index. Traditionally, NIIRS has been determined manually by specialized image analysts. We used the GIQE in order to reduce inefficiency and high costs cause by manual interpretation and to produce accurate NIIRS. For monitoring image degradation, we estimated GIQE physical parameters from image analysis and carried out time series analysis about the quality of the KOMPSAT-1 images. On all of the tests, we were able to identify the image degradation due to the changing time. This indicates that NIIRS derived from GIQE will be used for image degradation indicator.

• Korean Journal of Remote Sensing
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• v.10 no.1
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• pp.43-53
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• 1994

By Using MOS-1 satellite image(taken on 24 April 1990, after slash and burn), Shifting cultivation areas were estimated for the sub-basin area. In tropical region to analyse the correlation between shifting cultivation rate and bifurcation rate network which was calculated from topographic map, PC Arc - Info and IDRISI GIS software were used. As the distribution rate of shifting cultivation increases, the bifurcation rate is high. From the correlation analysis between the shifting cultivation and drainage network, it was found that shifting cultivation leads to land degradation and head erosion at the stream valley. To prevent such problems, it is mecessary that shifting cultivation areas should be converted to permanent paddy fields.