TY - JOUR
T1 - Hybrid consensus theoretic classification
AU - Benediktsson, Jon Atli
AU - Sveinsson, Jóhannes Rúnar
AU - Swain, Philip H.
N1 - Funding Information: Manuscript received September 13, 1996; revised March 3, 1997. This work was supported in part by the Icelandic Research Council and the Research Fund of the University of Iceland. J. A. Benediktsson and J. R. Sveinsson are with the Engineering Research Institute, University of Iceland, 107 Reykjavik, Iceland (e-mail: [email protected]). P. H. Swain is with the Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907 USA. Publisher Item Identifier S 0196-2892(97)04474-4.
PY - 1997
Y1 - 1997
N2 - Hybrid classification methods based on consensus from several data sources are considered. Each data source is at first treated separately and modeled using statistical methods. Then weighting mechanisms are used to control the influence of each data source in the combined classification. The weights are optimized in order to improve the combined classification accuracies. Both linear and nonlinear optimization methods are considered and used in classification of two multisource remote sensing and geographic data sets. A nonlinear method which utilizes a neural network gives excellent experimental results. The hybrid statistical/neural method outperforms all other methods in terms of test accuracies in the experiments.
AB - Hybrid classification methods based on consensus from several data sources are considered. Each data source is at first treated separately and modeled using statistical methods. Then weighting mechanisms are used to control the influence of each data source in the combined classification. The weights are optimized in order to improve the combined classification accuracies. Both linear and nonlinear optimization methods are considered and used in classification of two multisource remote sensing and geographic data sets. A nonlinear method which utilizes a neural network gives excellent experimental results. The hybrid statistical/neural method outperforms all other methods in terms of test accuracies in the experiments.
UR - https://www.scopus.com/pages/publications/0031186661
U2 - 10.1109/36.602526
DO - 10.1109/36.602526
M3 - Article
SN - 0196-2892
VL - 35
SP - 833
EP - 843
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 4
ER -