URBAN SPACE STRUCTURE ANALYSIS BY REMOTE SENSING : Urban Space Classification of Tokyo by Landsat Data
نویسندگان
چکیده
منابع مشابه
Urban Sensing by Hyperspectral Data
The airborne hyperspectral imaging data has operationally been used in environment, geology, agriculture and other fields. In this paper, airborne hyperspectral data in visible, SWIR, MIR and TIR spectral region acquired by the 128 band Operational Modular Imaging Spectrometer (OMIS) was attempt in urban object identification. Natural grassland/artificial grasslands, and different types of meta...
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Quantitative identification of physical changes, developments and dynamic position of urban green space is considered as the first step in its planning. By means of the aerial photos taken in 1956, 1974, and 1994 as well as the Quick Bird satellite image captured in 2006, this study has dealt with changes in per capita green space in Khorramabad during these years. First, geometric corrections ...
متن کاملEvaluation of Changes in Per Capita Green Space through Remote Sensing Data
Quantitative identification of physical changes, developments and dynamic position of urban green space is considered as the first step in its planning. By means of the aerial photos taken in 1956, 1974, and 1994 as well as the Quick Bird satellite image captured in 2006, this study has dealt with changes in per capita green space in Khorramabad during these years. First, geometric corrections ...
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The main purpose of urban design is to create good and high-quality urban spaces and environments for people to live while such quality may not be determined only by imposing a structural, perceptual and value system of the designer. It can be said that human and his powers to perceive surrounding environments are the focus of urban design. Having reviewed previous researches and theories in re...
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Kernel Principal Component Analysis (KPCA) is investigated for feature extraction from hyperspectral remote-sensing data. Features extracted using KPCA are classified using linear support vector machines. In one experiment it is shown that kernel principal component features are more linearly separable than features extracted with conventional principal component analysis. In a second experimen...
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ژورنال
عنوان ژورنال: Transactions of the Architectural Institute of Japan
سال: 1981
ISSN: 0387-1185,2433-0027
DOI: 10.3130/aijsaxx.305.0_122