

Satellite(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 classi-
     fication, 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 gener-
     ated 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 sec-
     tion (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. Conver-
     sion 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 interpreta-
     tion 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 max-
     imum- 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.

