wilt
OpenML dataset with id 40983
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/18151926/wilt.arff
Upload date: 4 December 2017
Dataset Characteristics
Number of classes: 2
Number of features: 6 (numeric: 5, symbolic: 1 and in total binary: 1 )
Number of instances: 4,839
Number of instances with missing values: 0
Number of missing values: 0
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.
__Changes w.r.t. version 1: renamed variables such that they match description.__
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.
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