Abstract
To apply decision level fusion to hyperspectral remote sensing (HRS) image
classification, three decision level fusion strategies are experimented on and
compared, namely, linear consensus algorithm, improved evidence theory, and the
proposed support vector machine (SVM) combiner. To evaluate the effects of the input
features on classification performance, four schemes are used to organize input
features for member classifiers. In the experiment, by using the operational modular
imaging spectrometer (OMIS) II HRS image, the decision level fusion is shown as an
effective way for improving the classification accuracy of the HRS image, and the
proposed SVM combiner is especially suitable for decision level fusion. The results
also indicate that the optimization of input features can improve the classification
performance.
© 2011 Chinese Optics Letters
PDF Article
More Like This
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription