The support vector machine is a group of relatively novel statistical learning algorithms that
have not been extensively exploited in the remote sensing community. In previous studies …

Land cover mapping is an important activity leading to the generation of various thematic
products essential for numerous environmental monitoring and resources management …

This letter presents the results of two different ensemble approaches to increase the
accuracy of land cover classification using support vector machines. Finite ensemble …

Support vector machine (SVM) was applied for land-cover characterization using MODIS
time-series data. Classification performance was examined with respect to training sample …

Support Vector Machines (SVMs) are a relatively new supervised classification technique to
the land cover mapping community. They have their roots in Statistical Learning Theory and …

The support vector machine (SVM) is a group of theoretically superior machine learning
algorithms. It was found competitive with the best available machine learning algorithms in …

Information about the Earth's surface is required in many wide-scale applications. Land
cover/use classification using remotely sensed images is one of the most common …

SVMs were initially developed to perform binary classification; though, applications of binary
classification are very limited. Most of the practical applications involve multiclass …

Support Vector Machines (SVMs) are a relatively new supervised classification technique to
the land cover mapping community. They have their roots in Statistical Learning Theory and …

Remote sensing analyses frequently use feature selection methods to remove non-
beneficial feature variables from the input data, which often improve classification accuracy …