FILE NAMES
	sat.trn - training set
	sat.tst - test set
	
	!!! NB. DO NOT USE CROSS-VALIDATION WITH THIS DATASET !!!
		Just train and test only once with the above
		training and test sets.
		
PURPOSE
	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. In
	the sample database, the class of a pixel is coded as
	a number.

PROBLEM TYPE
	Classification

AVAILABLE
	This database was generated from Landsat Multi-Spectral
	Scanner image data. These and other forms of remotely
	sensed imagery can be purchased at a price from relevant
	governmental authorities. The data is usually in binary
	form, and distributed on magnetic tape(s).

SOURCE
	The small sample database was provided by:
	Ashwin Srinivasan
	Department of Statistics and Modelling Science
	University of Strathclyde
	Glasgow
	Scotland
	UK

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.

DESCRIPTION
	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 number is a code for the following classes:

	Number			Class

	1			red soil
	2			cotton crop
	3			grey soil
	4			damp grey soil
	5			soil with vegetation stubble
	6			mixture class (all types present)
	7			very damp grey soil
	
	NB. There are no examples with class 6 in this dataset.
	
	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.

NUMBER OF EXAMPLES
	training set     4435
	test set         2000

NUMBER OF ATTRIBUTES
	36 (= 4 spectral bands x 9 pixels in neighbourhood )

ATTRIBUTES
	The attributes are numerical, in the range 0 to 255.

CLASS
	There are 6 decision classes: 1,2,3,4,5 and 7.

	NB. There are no examples with class 6 in this dataset-
	they have all been removed because of doubts about the 
	validity of this class.
	
AUTHOR
	Ashwin Srinivasan
     Department of Statistics and Data Modeling
     University of Strathclyde
     Glasgow
     Scotland
     UK
     ross@uk.ac.turing