http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
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.
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.