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Wilt Data Set
Download: Data Folder, Data Set Description

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.

Data Set Characteristics:  

Multivariate

Number of Instances:

4889

Area:

Life

Attribute Characteristics:

N/A

Number of Attributes:

6

Date Donated

2014-03-13

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

27712


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!

Files
training.csv: training data set (4339 image segments)
testing.csv: testing data set (500 image segments)


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.



Citation Request:

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.


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