  SVMs were initially developed to perform binary classification; though,
applications of binary classification are very limited. Most of the practical
applications involve multiclass classification, especially in remote sensing
land cover classification. A number of methods have been proposed to implement
SVMs to produce multiclass classification. A number of methods to generate
multiclass SVMs from binary SVMs have been proposed by researchers and is still
a continuing research topic. This paper compares the performance of six
multi-class approaches to solve classification problem with remote sensing data
in term of classification accuracy and computational cost. One vs. one, one vs.
rest, Directed Acyclic Graph (DAG), and Error Corrected Output Coding (ECOC)
based multiclass approaches creates many binary classifiers and combines their
results to determine the class label of a test pixel. Another catogery of multi
class approach modify the binary class objective function and allows
simultaneous computation of multiclass classification by solving a single
optimisation problem. Results from this study conclude the usefulness of One
vs. One multi class approach in term of accuracy and computational cost over
other multi class approaches.
