Satellite: Landsat Multi-Spectral Scanner Image Data
Description
The database consists of the multi-spectral values
of pixels in 3x3 neighbourhoods in a satellite image,
and the classification associated with the central pixel
in each neighbourhood. The aim is to predict this
classification, given the multi-spectral values.
Usage
data(Satellite)
Arguments
format
A data frame with 36 inputs (x.1 ...x.36) and one target
(classes).
Details
One frame of Landsat MSS imagery consists of four digital images
of the same scene in different spectral bands. Two of these are
in the visible region (corresponding approximately to green and
red regions of the visible spectrum) and two are in the (near)
infra-red. Each pixel is a 8-bit binary word, with 0 corresponding
to black and 255 to white. The spatial resolution of a pixel is about
80m x 80m. Each image contains 2340 x 3380 such pixels.
The database is a (tiny) sub-area of a scene, consisting of 82 x 100
pixels. Each line of data corresponds to a 3x3 square neighbourhood
of pixels completely contained within the 82x100 sub-area. Each line
contains the pixel values in the four spectral bands
(converted to ASCII) of each of the 9 pixels in the 3x3 neighbourhood
and a number indicating the classification label of the central
pixel.
The classes are
l{
red soil
cotton crop
grey soil
damp grey soil
soil with vegetation stubble
very damp grey soil
}
The data is given in random order and certain lines of data
have been removed so you cannot reconstruct the original image
from this dataset.
In each line of data the four spectral values for the top-left
pixel are given first followed by the four spectral values for
the top-middle pixel and then those for the top-right pixel,
and so on with the pixels read out in sequence left-to-right and
top-to-bottom. Thus, the four spectral values for the central
pixel are given by attributes 17,18,19 and 20. If you like you
can use only these four attributes, while ignoring the others.
This avoids the problem which arises when a 3x3 neighbourhood
straddles a boundary.
Origin{
The original Landsat data for this database was generated
from data purchased from NASA by the Australian Centre
for Remote Sensing, and used for research at:
The Centre for Remote Sensing, University of New South Wales,
Kensington, PO Box 1, NSW 2033, Australia.
The sample database was generated taking a small section (82
rows and 100 columns) from the original data. The binary values
were converted to their present ASCII form by Ashwin Srinivasan.
The classification for each pixel was performed on the basis of
an actual site visit by Ms. Karen Hall, when working for Professor
John A. Richards, at the Centre for Remote Sensing at the University
of New South Wales, Australia. Conversion to 3x3 neighbourhoods and
splitting into test and training sets was done by Alistair Sutherland.
}
History{
The Landsat satellite data is one of the many sources of information
available for a scene. The interpretation of a scene by integrating
spatial data of diverse types and resolutions including multispectral
and radar data, maps indicating topography, land use etc. is expected
to assume significant importance with the onset of an era characterised
by integrative approaches to remote sensing (for example, NASA's Earth
Observing System commencing this decade). Existing statistical methods
are ill-equipped for handling such diverse data types. Note that this
is not true for Landsat MSS data considered in isolation (as in
this sample database). This data satisfies the important requirements
of being numerical and at a single resolution, and standard maximum-
likelihood classification performs very well. Consequently,
for this data, it should be interesting to compare the performance
of other methods against the statistical approach.
}
These data have been taken from the UCI Repository Of Machine Learning
Databases at
ftp.ics.uci.edu://pub/machine-learning-databases
http://www.ics.uci.edu/mlearn/MLRepository.html
and were converted to R format by Friedrich.Leisch@ci.tuwien.ac.at.
datasets