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.