Abstract: This paper proposes to use ETM+ multispectral data
and panchromatic band as well as texture features derived from the
panchromatic band for land cover classification. Four texture features
including one 'internal texture' and three GLCM based textures
namely correlation, entropy, and inverse different moment were used
in combination with ETM+ multispectral data. Two data sets
involving combination of multispectral, panchromatic band and its
texture were used and results were compared with those obtained by
using multispectral data alone. A decision tree classifier with and
without boosting were used to classify different datasets. Results
from this study suggest that the dataset consisting of panchromatic
band, four of its texture features and multispectral data was able to
increase the classification accuracy by about 2%. In comparison, a
boosted decision tree was able to increase the classification accuracy
by about 3% with the same dataset.