\HeaderA{Satellite}{Landsat Multi-Spectral Scanner Image Data}{Satellite}
\keyword{datasets}{Satellite}
\begin{Description}\relax
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.
\end{Description}
\begin{Usage}
\begin{verbatim}data(Satellite)\end{verbatim}
\end{Usage}
\begin{Format}\relax
A data frame with 36 inputs (\code{x.1 ... x.36}) and one target
(\code{classes}).
\end{Format}
\begin{Details}\relax
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
\Tabular{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.
\end{Details}
\begin{Section}{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.
\end{Section}
\begin{Section}{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.
\end{Section}
\begin{Source}\relax
Ashwin Srinivasan,
Department of Statistics and Data Modeling,
University of Strathclyde,
Glasgow,
Scotland,
UK,
\email{ross@uk.ac.turing}

These data have been taken from the UCI Repository Of Machine Learning
Databases at
\Itemize{
\item \url{ftp://ftp.ics.uci.edu/pub/machine-learning-databases}
\item \url{http://www.ics.uci.edu/~mlearn/MLRepository.html}
}
and were converted to R format by Friedrich.Leisch@ci.tuwien.ac.at.
\end{Source}

