Satellite {mlbench}R Documentation

Landsat Multi-Spectral Scanner Image Data

Description

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.

Usage

data(Satellite)

Format

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

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.

Source

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

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