Landsat Multi-Spectral Scanner Image Data
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.
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
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.
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.
These data have been taken from the UCI Repository Of Machine Learning Databases at
A data frame with 36 inputs (
x.1 ...x.36) and one target