Abstract
Supervised learning methods such as Maximum Likelihood
(ML) are often used in land cover (thematic) classification
of remote sensing imagery. ML classifier relies
exclusively on spectral characteristics of thematic classes
whose statistical distributions are often overlapping. The
spectral response distributions of thematic classes are dependent
on many factors including elevation, soil types, and
atmospheric conditions present at the time of data acquisition.
A second problem with statistical classifiers is the
requirement of large number of accurate training samples,
which are often costly and time consuming to acquire over
large geographic regions. With the increasing availability
of geospatial databases, it is possible to exploit the knowledge
derived from these ancillary datasets to improve classification
accuracies even when the class distributions are
highly overlapping. Likewise newer semi-supervised techniques
can be adopted to improve the parameter estimates
of statistical model by utilizing a large number of easily
available unlabeled training samples. Unfortunately there
is no convenient multivariate statistical model that can be
employed for mulitsource geospatial databases. In this paper
we present a hybrid semi-supervised learning algorithm
that effectively exploits freely available unlabeled training
samples from multispectral remote sensing images and also
incorporates ancillary geospatial databases. We have conducted
several experiments on real datasets, and our new
hybrid approach shows over 15% improvement in classification
accuracy over conventional classification schemes.
