Improving specific class mapping from remotely sensed data by cost-sensitive learning

In many remote-sensing projects, one is usually interested in a small
number of land-cover classes present in a study area and not in all the
land-cover classes that make-up the landscape. Previous studies in
supervised classification of satellite images have tackled specific class
mapping problem by isolating the classes of interest and combining all
other classes into one large class, usually called others, and by developing
a binary classifier to discriminate the class of interest from the
others. Here, this approach is called focused approach. The strength of
the focused approach is to decompose the original multi-class supervised
classification problem into a binary classification problem, focusing
the process on the discrimination of the class of interest. Previous
studies have shown that this method is able to discriminate more
accurately the classes of interest when compared with the standard
multi-class supervised approach. However, it may be susceptible to
data imbalance problems present in the training data set, since the
classes of interest are often a small part of the training set. A result the
classification may be biased towards the largest classes and, thus, be
sub-optimal for the discrimination of the classes of interest. This study
presents a way to minimize the effects of data imbalance problems in
specific class mapping using cost-sensitive learning. In this approach
errors committed in theminority class are treated as being costlier than
errors committed in the majority class. Cost-sensitive approaches are
typically implemented by weighting training data points accordingly
to their importance to the analysis. By changing the weight of individual
data points, it is possible to shift theweight from the larger classes
to the smaller ones, balancing the data set. To illustrate the use of the
cost-sensitive approach to map specific classes of interest, a series of
experiments with weighted support vector machines classifier and
Landsat Thematic Mapper data were conducted to discriminate two
types of mangrove forest (high-mangrove and low-mangrove) in
Saloum estuary, Senegal, a United Nations Educational, Scientific and
Cultural Organisation World Heritage site. Results suggest an increase
in overall classification accuracy with the use of cost-sensitive method
(97.3%) over the standard multi-class (94.3%) and the focused
approach (91.0%). In particular, cost-sensitive method yielded higher
sensitivity and specificity values on the discrimination of the classes of
interest when compared with the standard multi-class and focused
approaches.