Introduction
Remote sensing has increasingly been used as a source of information for characterizing land use and land cover change at local, regional and global scales (Townshed and Justice, 2002; Lunetta and Lyons, 2003 in Jensen, 2005, p. 337). Land use/land cover classification based on statistical pattern recognition most often used methods of information extraction (Narumalani et al., 2002 in Jensen, 2005, p. 337). In this study, unsupervised and supervised classifications were carried out for land-cover mapping of a remotely sensed imagery of London.
Objectives of study
● To classify an image using both supervised and unsupervised techniques, including:
i) Evaluation of which Landsat bands to include in a classification
ii)Analysis of signature separability
iii)Class aggregation
iv)Inclusion of ancillary data
● To interpret the result of the classification using accuracy calculations.
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