| 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

1, 2, 3, 4, 5, 7.

TL1: continuous
TL2: continuous
TL3: continuous
TL4: continuous
TM1: continuous
TM2: continuous
TM3: continuous
TM4: continuous
TR1: continuous
TR2: continuous
TR3: continuous
TR4: continuous
ML1: continuous
ML2: continuous
ML3: continuous
ML4: continuous
MM1: continuous
MM2: continuous
MM3: continuous
MM4: continuous
MR1: continuous
MR2: continuous
MR3: continuous
MR4: continuous
BL1: continuous
BL2: continuous
BL3: continuous
BL4: continuous
BM1: continuous
BM2: continuous
BM3: continuous
BM4: continuous
BR1: continuous
BR2: continuous
BR3: continuous
BR4: continuous
