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영상합성을 통한 KOMPSAT - 1 EOC 의 분류정확도 및 환경정보 추출능력 향사
하성룡(Sung Ryong Ha),박대희(Dae Hee Park),박상영(Sang Young Park) 한국지리정보학회 2002 한국지리정보학회지 Vol.5 No.2
Classification of the land cover characteristics is a major application of remote sensing. The goal of this study is to propose an optimal classification process for electro-optical camera(EOC) of Korea Multi-Purpose Satellite(KOMPSAT). The study was carried out on Landsat TM, high spectral resolution image and KOMPSAT EOC, high spatial resolution image of Miho river basin, Korea. The study was conducted in two stages: one was image fusion of TM and EOC to gain high spectral and spatial resolution image, the other was land cover classification on fused image. Four fusion techniques were applied and compared for its topographic interpretation such as IHS, HPF, CN and wavelet transform The fused images were classified by radial basis function neural network(RBF-NN) and artificial neural network(ANN) classification model. The proposed RBF-NN was validated for the study area and the optimal model structure and parameter were respectively identified for different input band combinations. The results of the study propose an optimal classification process of KOMPSAT EOC to improve the thematic mapping and extraction of environmental information.
하성룡,Ha, Sung Ryong 대한토목학회 1994 대한토목학회논문집 Vol.14 No.4
본 연구는 유역의 지형인자를 고려한 강우의 수리학적 단기유출 해석 시스템을 개발한 것이다. 강우 유출의 기본 개념은 Kinematic Wave 이론에 의거하였으며, 그 수치해는 특성곡선 추적법에 의하여 산출된다. 개발된 강우유출해석 시스템은 한개의 하도를 중심으로 좌우 2개의 등가사면을 지니는 단위 등가조도 모델이 복수개의 하도망을 따라 결합된 복합 등가조도 유역 모델로 구성되며, 등가조도유역 모델은 유역의 하천차수이론에 근거하여 규정됨으로써 유역이 지니는 확률적 지형인자를 모델 파라메타에 함축시키는 특성을 지닌다. 모델 파라메타의 민감도분석과 IHP 대표유역인 보청천 유역의 지형 및 수문자료를 이용한 모델 보정과 검정을 통하여 제안 시스템의 현장 적용성과 재현가능성이 확인되었다. 본 연구의 성과에 의하여 해석대상 등가유역의 시공간상 임의 위치에서 수리량의 시간변동 예측 및 유역의 개발에 따른 단기적 수질변동 해석에 요구되는 수리량의 해석이 가능하게 되었다. This study is to develop an advanced storm runoff analysis program which takes geomorphological characteristics of watershed into consideration in determining model parameters. Basic concept of storm runoff modelling is based upon the kinematic wave theory. And numerical solution is obtained by the characteristic curve method. The storm runoff analysis program developed by this study is composed of multiple equivalent roughness sub-basins, each of which has two equivalent catchments on both side of a stream. Because it is based upon the stream-order of the Strahler system, the equivalent catchment-stream network reflects the stochastic geomorphological characteristics in the model parameter. Applicability and reliability of the storm runoff analysis program is evidenced by model calibration and verification process utilizing geographical and hydrological data of the Bocheong-river area which is a representative watershed of IHP projects in Korea. This study will hopefully contribute to hydrological calculation essentially required to understand water quality effect caused by regional development.
훈련지역의 취득방법 및 규모에 따른 JERS - 1 위성영상의 토지피복분류 정확도 평가
하성룡(Sung Ryong Ha),박상영(Sang Young Park),박대희(Dae Hee Park),경천구(Chon Ku Kyoung) 한국지리정보학회 2002 한국지리정보학회지 Vol.5 No.1
The classification accuracy of land cover has been considered as one of the major issues to estimate pollution loads generated from diffuse landuse patterns in a watershed. This research aimed to assess the effects of the acquisition methods and sampling size of training reference data on the classification accuracy of land cover using an imagery acquired by optical sensor(OPS) on JERS-1. Two kinds of data acquisition methods were considered to prepare training data. The first was to assign a certain land cover, type to a specific pixel based on the researchers subjective discriminating capacity about current land use and the second was attributed to an aerial photograph incorporated with digital maps with GIS. Three different sizes of samples, 0.3%, 0.5%, and 1.0% of all pixels, were applied to examine the consistency of the classified land cover with the training data of corresponding pixels. Maximum likelihood scheme was applied to classify the land use patterns of JERS-1 imagery. Classification run applying an aerial photograph achieved 18% higher consistency with the training data than the run applying the researchers subjective discriminating-capacity. Regarding the sample size, it was proposed that the size of training area should be selected at least over 1% of all of the pixels in the study area in order to obtain the accuracy with 95% for JERS-1 satellite imagery on a typical small-to-medium-size urbanized area.