城市土地覆盖数据集

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.