1570 wilt 1 **Author**: Brian Johnson **Source**: [UCI] (https://archive.ics.uci.edu/ml/datasets/Wilt) **Please cite**: Johnson, B., Tateishi, R., Hoan, N., 2013. A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees. International Journal of Remote Sensing, 34 (20), 6969-6982. ### Dataset: Wilt Data Set ### Abstract: High-resolution Remote Sensing data set (Quickbird). Small number of training samples of diseased trees, large number for other land cover. Testing data set from stratified random sample of image. ### Source: Brian Johnson; Institute for Global Environmental Strategies; 2108-11 Kamiyamaguchi, Hayama, Kanagawa,240-0115 Japan; Email: Johnson '@' iges.or.jp ### Data Set Information: This data set contains some training and testing data from a remote sensing study by Johnson et al. (2013) that involved detecting diseased trees in Quickbird imagery. There are few training samples for the 'diseased trees' class (74) and many for 'other land cover' class (4265). The data set consists of image segments, generated by segmenting the pansharpened image. The segments contain spectral information from the Quickbird multispectral image bands and texture information from the panchromatic (Pan) image band. The testing data set is for the row with “Segmentation scale 15” segments and “original multi-spectral image” Spectral information in Table 2 of the reference (i.e. row 5). Please see the reference below for more information on the data set, and please cite the reference if you use this data set. Enjoy! ### Attribute Information: class: 'w' (diseased trees), 'n' (all other land cover) GLCM_Pan: GLCM mean texture (Pan band) Mean_G: Mean green value Mean_R: Mean red value Mean_NIR: Mean NIR value SD_Pan: Standard deviation (Pan band) ### Relevant Papers: Johnson, B., Tateishi, R., Hoan, N., 2013. A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees. International Journal of Remote Sensing, 34 (20), 6969-6982. 1 ARFF 2015-06-01T23:34:28 Public https://api.openml.org/data/v1/download/1675983/wilt.arff https://openml1.win.tue.nl/datasets/0000/1570/dataset_1570.pq 1675983 Class OpenML100study_123study_14study_34study_52study_7 public https://openml1.win.tue.nl/datasets/0000/1570/dataset_1570.pq deactivated 2018-10-03 22:03:05 f56f95bac28580e0750b515885efbf49