Commit Inicial

data/tanveer/statlog-landsat/sat.doc

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+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
+
+
+