

   LLaannddssaatt MMuullttii--SSppeeccttrraall SSccaannnneerr IImmaaggee DDaattaa

        data(satellite)

   FFoorrmmaatt::

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

   DDeessccrriippttiioon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.

   OOrriiggiin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Hiissttoorry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Soouurrcce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.

