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').

_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

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

        *  http://www.ics.uci.edu/mlearn/MLRepository.html

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

