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城市土地覆盖数据集

城市土地覆盖数据集

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Physical Classification

Brian Johnson; Institute for Global Environmental Strategies; 2108-11 Kamiyamaguchi, Hayama, Kanagawa,240-0115 Japan; Em......

数据结构 ? 142K

    Data Structure ?

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    README.md

    Brian Johnson;
    Institute for Global Environmental Strategies;
    2108-11 Kamiyamaguchi, Hayama, Kanagawa,240-0115 Japan;
    Email: Johnson '@' iges.or.jp


    Data Set Information:

    Contains training and testing data for classifying a high resolution aerial image into 9 types of urban land cover. Multi-scale spectral, size, shape, and texture information are used for classification. There are a low number of training samples for each class (14-30) and a high number of classification variables (148), so it may be an interesting data set for testing feature selection methods. The testing data set is from a random sampling of the image.

    Class is the target classification variable. The land cover classes are: trees, grass, soil, concrete, asphalt, buildings, cars, pools, shadows.


    Attribute Information:

    LEGEND
    Class: Land cover class (nominal)
    BrdIndx: Border Index (shape variable)
    Area: Area in m2 (size variable)
    Round: Roundness (shape variable)
    Bright: Brightness (spectral variable)
    Compact: Compactness (shape variable)
    ShpIndx: Shape Index (shape variable)
    Mean_G: Green (spectral variable)
    Mean_R: Red (spectral variable)
    Mean_NIR: Near Infrared (spectral variable)
    SD_G: Standard deviation of Green (texture variable)
    SD_R: Standard deviation of Red (texture variable)
    SD_NIR: Standard deviation of Near Infrared (texture variable)
    LW: Length/Width (shape variable)
    GLCM1: Gray-Level Co-occurrence Matrix [i forget which type of GLCM metric this one is] (texture variable)
    Rect: Rectangularity (shape variable)
    GLCM2: Another Gray-Level Co-occurrence Matrix attribute (texture variable)
    Dens: Density (shape variable)
    Assym: Assymetry (shape variable)
    NDVI: Normalized Difference Vegetation Index (spectral variable)
    BordLngth: Border Length (shape variable)
    GLCM3: Another Gray-Level Co-occurrence Matrix attribute (texture variable)

    Note: These variables repeat for each coarser scale (i.e. variable_40, variable_60, ...variable_140).


    Relevant Papers:

    1. Johnson, B., Xie, Z., 2013. Classifying a high resolution image of an urban area using super-object information. ISPRS Journal of Photogrammetry and Remote Sensing, 83, 40-49.

    2. Johnson, B., 2013. High resolution urban land cover classification using a competitive multi-scale object-based approach. Remote Sensing Letters, 4 (2), 131-140.



    Citation Request:

    Please cite:

    1. Johnson, B., Xie, Z., 2013. Classifying a high resolution image of an urban area using super-object information. ISPRS Journal of Photogrammetry and Remote Sensing, 83, 40-49.

    2. Johnson, B., 2013. High resolution urban land cover classification using a competitive multi-scale object-based approach. Remote Sensing Letters, 4 (2), 131-140.

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