• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.442-442
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• 2022

고해상도 시공간적 격자 형태의 레이더 강수는 돌발홍수(flash flood)와 같은 기상재해에 대비하기 위하여 실시간 예측정보로 활용된다. 그러나 대부분의 레이더 강수는 과소 추정되는 경향이 있어 정량적인 보정 과정인 QPE (Quantitative Precipitation Estimation)가 필요하다. 일반적으로 레이더 강수자료 보정은 지점 관측자료를 활용하지만, 본 연구에서는 지상 강수량 기반의 고해상도 격자 강수자료를 생산하여 레이더 강수자료와 직접적으로 비교하고자 한다. 이에 고도와 지형적 특성을 고려한 PRISM(Precipitation-elevation Regressions on Independent Slopes Model) 방법을 사용하여 고해상도 격자기반의 자료를 생성하였다. PRISM 방법은 고도와 지리정보를 독립변수로 갖는 회귀모형 기반의 기후인자 추정 모형이다. 생산된 고해상도 격자 강수자료와 레이더 강수자료를 QPF (Quantitative Precipitation Forecast) 모델의 입력자료로 사용하여 예측결과를 비교하였다. 해당 QPF 모델은 이류(advection)와 확률론적 섭동(stochastic perturbation)을 기반으로 하며, 강수 앙상블 자료를 생산한다. QPF 모델에 대해 투 트랙(two-track) 방법으로 생산된 예측정보를 통해 레이더 강수자료의 격자별 후처리 보정이 가능할 것으로 판단된다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.169-178
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• 2001

The demand for high-resolution images is gradually increasing, whereas many imaging systems have been designed to allow a certain level of aliasing during image acquisition. Thus, digital image processing approaches have recently been investigated to reconstruct a high-resolution image from aliased low-resolution images. However, since the sub-pixel motion information is assumed to be accurate in most conventional approaches, the satisfactory high-resolution image cannot be obtained when the sub-pixel motion information is inaccurate. Therefore, in this paper we propose a new algorithm to reduce the distortion in the reconstructed high-resolution image due to the inaccuracy of sub-pixel motion information. For this purpose, we analyze the effect of inaccurate sub-pixel motion information on a high-resolution image reconstruction, and model it as zero-mean additive Gaussian errors added respectively to each low-resolution image. To reduce the distortion we apply the modified multi-channel image deconvolution approach to the problem. The validity of the proposed algorithm is both theoretically and experimentally demonstrated in this paper.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.266-274
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• 2007

This paper proposes a new reconstruction method of high-resolution facial image from a low-resolution facial image based on top-down machine learning and recursive error back-projection. A face is represented by a linear combination of prototypes of shape and that of texture. With the shape and texture information of each pixel in a given low-resolution facial image, we can estimate optimal coefficients for a linear combination of prototypes of shape and those that of texture by solving least square minimizations. Then high-resolution facial image can be obtained by using the optimal coefficients for linear combination of the high-resolution prototypes. In addition, a recursive error back-projection procedure is applied to improve the reconstruction accuracy of high-resolution facial image. The encouraging results of the proposed method show that our method can be used to improve the performance of the face recognition by applying our method to reconstruct high-resolution facial images from low-resolution images captured at a distance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.125-132
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• 2017

Even after applying an image registration, Very High Resolution (VHR) multi-temporal images acquired from different optical satellite sensors such as IKONOS, QuickBird, and Kompsat-2 show a local misalignment due to dissimilarities in sensor properties and acquisition conditions. As the local misalignment, also referred to as Registration Noise (RN), is likely to have a negative impact on multi-temporal information extraction, detecting and reducing the RN can improve the multi-temporal image processing performance. In this paper, an approach to fine registration between VHR multi-temporal images by considering local distribution of RN is proposed. Since the dominant RN mainly exists along boundaries of objects, we use edge information in high frequency regions to identify it. In order to validate the proposed approach, datasets are built from VHR multi-temporal images acquired by optical satellite sensors. Both qualitative and quantitative assessments confirm the effectiveness of the proposed RN-based fine registration approach compared to the manual registration.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.547-552
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• 2007

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. The first one is to perform a global estimation of the least square error on the basis of a linear model of low-resolution image associated with high-resolution feature, and next local correction then makes the reconstructed image locally fit to the original spectral values. In this study, the new method was applied to KOMPSAT-1 EOC image of 6m and LANDSAT ETM+ of 30m, and an 1m RGB image was also generated from 4m IKONOS multispectral data. The results show its capability to reconstruct high-resolution imagery from multispectral data of low-resolution.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.04a
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• pp.83-87
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• 2004

본 연구는 고해상도의 충적층 지하수위 분포 조사를 위한 탄성파 굴절법 조사 방법을 소개하고 부여 군수리 충적층 일대에서 이 기법을 통해, 획득된 실제 충적층내의 지하수위 조사 결과를 제시한다. 기본적으로 본 연구에서 활용된 연속 굴절파 중합 방식은 동일 공심점(common mid point, 이후 CMP)을 갖는 굴절파 신호를 취합하고, 이격 거리(offset)에 대한 시간 지연 효과 보정을 수행한 후, 이들 신호를 중합하여, 충적층의 지하수위면에서 굴절된 신호를 보다 뚜렷이 부각시켜 정확한 지하수위 정보를 획득 하는 방식으로 일명 CMP 굴절법이라고도 한다. 이 방식은 독일에서 최초 개발되었으나(Gebrande, 1986; Orlowsky 등, 1998), 국내에서 적용되기는 본 연구가 최초이다. 이러한 탄성파의 굴절 신호를 사용하는 방식은 우선, 기존의 일반적인 고해상도 반사법 탐사에서 잡음으로 여겨졌던 굴절파 신호를 활용할 수 있으며, 고해상도 반사법 탐사와 동일한 배열과 운영 방식으로 획득된 자료에서 원하는 정보를 획득할 수 있으므로, 고해상도 반사법에 의한 기반암 조사와 함께 적용될 경우, 정화한 충적 대수층의 분포를 조사할 수 있게 하여주는 획기적인 조사 신기술이다. 개발된 기법은 부여 군수리 충적층 지역을 대상으로 적용되었으며, 그 결과 기존의 어떠한 지구물리 조사 방법보다 정확하고 분명한 지하수위 분포를 보여주었다.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.301-310
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• 2003

In this paper, we design and Implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the m()st effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12C
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• pp.1194-1200
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• 2006

In this paper, we propose an adaptive MAP (Maximum A Posteriori) high-resolution image reconstruction algorithm using local statistics. In order to preserve the edge information of an original high-resolution image, a visibility function defined by local statistics of the low-resolution image is incorporated into MAP estimation process, so that the local smoothness is adaptively controlled. The weighted non-quadratic convex functional is defined to obtain the optimal solution that is as close as possible to the original high-resolution image. An iterative algorithm is utilized for obtaining the solution, and the smoothing parameter is updated at each iteration step from the partially reconstructed high-resolution image is required. Experimental results demonstrate the capability of the proposed algorithm.

• Current Industrial and Technological Trends in Aerospace
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• v.5 no.2
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• pp.51-57
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• 2007

상용 위성탑재카메라를 이용한 지표면의 고해상도 영상획득은 1990년대부터 세계 각국에서 많은 노력과 투자를 하고 있다. 미국은 이미 해상도 1m급의 IKONOS, Orbview 및 Quickbird 등을 운용하고 있으며 최근에는 해상도 0.5m급 이하의 위성탑재 카메라를 개발하고 있는 것으로 알려졌다. 러시아, 프랑스, 이스라엘, 일본 등도 1m급 탑재카메라를 개발 중이거나 운용중이며 우리나라도 다목적 실용위성 시리즈의 탑재카메라 개발을 통해 고해상도 위성 카메라를 운용 및 개발 중이다. 이러한 개발동향에 따라 고해상도 위성카메라의 광학부품과 구조부품에 대한 기술적 연구와 개발에도 많은 노력이 이루어지고 있는데, 국내에서도 향후 계속되는 국가의 위성탑재카메라 개발계획에 따라 요구되는 핵심 광구조 부품 개발을 위해 대구경 광학 부품이나 구조물에 대해서 단계적인 국내개발을 시작하고 있다. 우선적으로 한국항공우주연구원과 한국표준과학연구원은 연구원간 협력프로그램으로 대구경 우주급 반사경조립체에 대한 국내개발을 진행하고 있으며 우주환경에서의 광학시험에 필요한 관련 시설을 구축하고 있으므로 국내의 위성관련 광구조부품 개발 기술도 획기적으로 향상될 것으로 기대된다.