Satellite              package:mlbench              R Documentation

_L_a_n_d_s_a_t _M_u_l_t_i-_S_p_e_c_t_r_a_l _S_c_a_n_n_e_r _I_m_a_g_e _D_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     data(Satellite)

_F_o_r_m_a_t:

     A data frame with 36 inputs ('x.1 ... x.36') and one target
     ('classes').

_D_e_t_a_i_l_s:

     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

       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.

_O_r_i_g_i_n:

     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.

_H_i_s_t_o_r_y:

     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.

_S_o_u_r_c_e:

     Ashwin Srinivasan, Department of Statistics and Data Modeling,
     University of Strathclyde, Glasgow, Scotland, UK,
     ross@uk.ac.turing

     These data have been taken from the UCI Repository Of Machine
     Learning Databases at

        *  <URL: ftp://ftp.ics.uci.edu/pub/machine-learning-databases>

        *  <URL: http://www.ics.uci.edu/~mlearn/MLRepository.html>

     and were converted to R format by
     Friedrich.Leisch@ci.tuwien.ac.at.

