• 대기
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• 제14권1호
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• pp.24-43
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• 2004

This paper describes the detailed characteristics of heavy rainfall events occurred in Chungcheong province on 15 and 16 April and from 6 to 8 August 2002 based on the analysis of raingauge rainfall rate and radar reflectivity from the METRI's X-band Weather Radar located in Cheongju. A synoptic analysis of the case is carried out, first, and then the analysis is devoted to seeing how the radar observes the case and how much information we obtain. The highly resolved radar reflectivity of horizontal and vertical resolutions of 1 km and 500 m, respectively shows a three-dimensional structure of the precipitating system, in a similar sequence with the ground rainfall rate. The radar echo classification algorithm for convective/stratiform cloud is applied. In the convectively-classified area, the radar reflectivity pattern shows a fair agreement with that of the surface rainfall rate. This kind of classification using radar reflectivity is considered to be useful for the precipitation forecasting. Another noteworthy aspect of the case includes the effect of topography on the precipitating system, following the analysis of the surface rainfall rate, topography, and precipitating system. The results from this case study offer a unique opportunity of the usefulness of weather radar for better understanding of structural and variable characteristics of flash flood-producing heavy rainfall events, in particular for their improved forecasting.

• 한국지능시스템학회논문지
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• 제9권5호
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• pp.472-479
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• 1999

본 논문에서는 뉴로-퍼지 알고리즘을 이용한 원격탐사 화상의 지표면 패턴분류기를 제안한다. 제안된 패턴 분류기는 일반적인 퍼지 인식기를 가지고 있는 3층 전방향 신경회로망 구조로 되어 있고 가중치들은 퍼지집합으로 구성된다. 이러한 퍼지-뉴로 패턴분류 시스템을 Visual C++ 환경을 구현한다. 성능평가를 위해 기존의 역전파 학습기능을 가진 신경회로망과 Maximum-likelihood 알고리즘을 이용해처리한 결과와비교분석한다. 대표적인 지표면 특징을 나타내는 8개의 클래스에 대해 훈련집합을 선정하고 각각의 분류 알고리즘에 같은 훈련집합을 사용하여 학습시킨 후 실험화상을 적용하여 지표면 특징을 8개의 클래스로 분류하였다. 실험결과 제안된 뉴로-퍼지 분류기는 여러개의 클래스로 혼합된 패턴에 대해서 기존의 분류기들에 비해 보다 더 좋은 성능을 보인다.

• 대한원격탐사학회지
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• 제24권3호
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• pp.235-243
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• 2008

Object tracking is a common vision task to detect and trace objects between consecutive frames. It is also important for a variety of applications such as surveillance, video based traffic monitoring system, and so on. An efficient moving vehicle clustering and tracking algorithm suitable for traffic monitoring system is proposed in this paper. First, automatic background extraction method is used to get a reliable background as a reference. The moving blob(object) is then separated from the background by mean shift method. Second, the scale invariant feature based method extracts the salient features from the clustered foreground blob. It is robust to change the illumination, scale, and affine shape. The simulation results on various road situations demonstrate good performance achieved by proposed method.

• 대한원격탐사학회지
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• 제37권1호
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• pp.153-160
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• 2021

Land Surface Temperature (LST) is the radiological surface temperature which observed by satellite. It is very important factor to estimate condition of the Earth such as Global warming and Heat island. For these reasons, many countries operate their own satellite to observe the Earth condition. South Korea has many landcovers such as forest, crop land, urban. Therefore, if we want to retrieve accurate LST, we would use high-resolution satellite data. In this study, we made LSTs with 4 LST retrieval algorithms which are used widely with Landsat-8 data which has 30 m spatial resolution. We retrieved LST using equations of Price, Becker et al. Prata, Coll et al. and they showed very similar spatial distribution. We validated 4 LSTs with Moderate resolution Imaging Spectroradiometer (MODIS) LST data to find the most suitable algorithm. As a result, every LST shows 2.160 ~ 3.387 K of RMSE. And LST by Prata algorithm show the lowest RMSE than others. With this validation result, we choose LST by Prata algorithm as the most suitable LST to South Korea.

• 대한원격탐사학회지
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• 제25권6호
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• 대한원격탐사학회지
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• 제24권3호
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• pp.251-256
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• 2008

Morphing method, one of classic image processing algorithms, has been used in various application fields. The motivation of this work is to investigate its applicability in consideration to geo-spatial data including airborne or space-borne images. For this purpose, the Beier and Neely morphing algorithm is tentatively implemented in the form of a prototype with user interface. As the results, this feature-based morphing with paired image sets can be used for general users: image simulation using two or more images and construction of color-blending image between source image and destination image in different types. Some simple application cases were demonstrated. This scheme is the simple and useful approach for those who want to utilize both geo-spatial data sets and airborne/space-borne image sets.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.628-632
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• 2005

Remote sensing through atmospheric turbulence had been hard works for a long time, because wavefront distortion due to the Earth's atmospheric turbulence deteriorates image quality. But due to the appearance of adaptive optics, it is no longer difficult things. Adaptive optics is the technology to correct random optical wavefront distortions in real time. For past three decades, research on adaptive optics has been performed actively. Currently, most of newly built telescopes have adaptive optical systems. Adaptive optical system is typically composed of three parts, wavefront sensing, wavefront correction and control. In this work, the wavefront sensing technology for adaptive optical system is treated. More specifically, shearing interferometers and Shack-Hartmann wavefront sensors are considered. Both of them are zonal wavefront sensors and measure the slope of a wavefront. . In this study, the shearing interferometer is made up of four right-angle prisms, whose relative sliding motions provide the lateral shearing and phase shifts necessary for wavefront measurement. Further, a special phase-measuring least-squares algorithm is adopted to compensate for the phase-shifting error caused by the variation in the thickness of the index-matching oil between the prisms. Shack-Hartmann wavefront sensors are widely used in adaptive optics for wavefront sensing. It uses an array of identical positive lenslets. And each lenslet acts as a subaperture and produces spot image. Distortion of an input wavefront changes the location of spot image. And the slope of a wavefront is obtained by measuring this relative deviation of spot image. Structures and measuring algorithms of each sensor will be presented. Also, the results of wavefront measurement will be given. Using these wavefront sensing technology, an adaptive optical system will be built in the future.

• 대한원격탐사학회지
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• 제18권6호
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• pp.359-367
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• 2002

본 논문에서는 효율적인 영상 부호화를 위해 분할계수의 상관관계를 이용한 새로운 웨이블릿 패킷 영상 부호화기 알고리즘을 제안한다. 제안된 웨이블릿 패킷 영상 부호화기 알고리즘은 주파수 부대역간의 상관관계를 이용하여 계수분할 순서(CPSO, Coefficient Partitioning Scanning Order)를 정의한 후, 이를 이용하여 새로운 부모-자식 노드 관계를 도출하고, 도출된 부모-자식 노드 관계를 이용하여 계수들을 제로트리 방식으로 부호화함으로써 영상 복원시 오차를 감소시켰다. 그 결과 기존의 알고리즘들과 비교하여 비트율과 제곱 오차에 대해서 성능개선이 이루어 졌고, 응용분야에 따라 비트율 조정을 쉽게 제어할 수 있는 능력을 입증하였다. 특히 항공사진이나 위성사진 중 도시 중심부나 농경지 등과 같이 중고주파 대역성분이 많이 포함된 영상의 경우, 기존 알고리즘에 비해 처리 방법이 간단하면서도 정확한 결과를 보여준다. 또한 자료 영상들에 대한 실험 결과를 통해서, 제안한 알고리즘이 작은 파일크기에서도 고해상도를 요구하는 실시간 시스템이나 온라인 영상처리, 영상합성 분야에 적용될 수 있는 가능성이 있음을 증명하고자 하였다.

• 한국전자통신학회논문지
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• 제15권6호
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• pp.1209-1216
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• 2020

본 연구에서는 무인항공기 원격탐사 기법과 딥러닝 기반 객체인식 알고리즘을 활용한 해안표착폐기물 탐지기법을 제안한다. 항공영상 내에 존재하는 해안표착폐기물을 탐지하기 위해 심층신경망 기반 객체 인식 알고리즘을 제안하였다. PET, 스티로폼, 기타 플라스틱의 3가지 클래스의 이미지 데이터셋으로 심층신경망 모델을 훈련시켰으며, 각 클래스별 탐지 정확도를 Darknet-53과 비교하였다. 이를 통해 해안표착 폐기물을 무인항공기를 통해 성상별 모니터링할 수 있었으며, 향후 본 연구에서 제안하는 방법이 적용될 경우 해변 전체에 대한 성상별 전수조사가 가능하며, 이를 통해 해양환경 감시 분야의 효율성 증대에 기여할 수 있을 것으로 판단된다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.12-16
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• 2002

The clear sky radiative forcings of aerosols were evaluated over East Asia. We first investigated optical characteristics of aerosol using sky radiation measurements. An algorithm of Nakajima et al. (1996) is used for retrieving aerosol parameters such as optical thickness, ${\AA}$ngstr$\"{O}$m exponent, single scattering albedo, and size distribution from sky-radiation measurements, which then can be used for examining spatial and temporal variations of aerosol. Obtaining aerosol radiative forcing at TOA and surface, a radiative transfer model is used with inputs of obtained aerosol parameters and GMS-5 satellite-based cloud optical properties. Results show that there is a good agreement of simulated downwelling radiative flux at the surface with observation within 10 W m$^{-2}$ rms errors under the clear sky condition. However, a relatively large difference up to 40 W m$^{-2}$ rms error is found under the cloudy sky condition. The computed aerosol radiative forcing at the surface shows downward flux changes ranging from -100 to -170 W m$^{-2}$ per unit aerosol optical thickness at 0.7 $\mu$m. The different values of aerosol radiative forcing among the stations is mainly due to the differences in single scattering albedo ($\omega$$_{0.7}$) and asymmetric parameter (g$_1$) related to the geographical and seasonal variations.