Name
Abstract | ||
|---|---|---|
An elliptical basis function (EBF) network is employed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and employing the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture-density distributions in the feature space, the network not only possesses the advantage of the RBF mechanism, but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is more effective in training and simpler in structure than an RBF network constructed for the same task. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/s10109-004-0136-1 | Journal of Geographical Systems |
Keywords | Field | DocType |
Neural networks,classification,elliptical basis functions,EM algorithm,mixture densities,radial basis functions,remotely sensed image,R14,R52,Q24 | Radial basis function,Computer science,Matrix (mathematics),Remote sensing,Basis function,Artificial intelligence,Contextual image classification,Artificial neural network,Covariance,Feature vector,Pattern recognition,Expectation–maximization algorithm,Statistics | Journal |
Volume | Issue | Citations |
6 | 3 | 2 |
PageRank | References | Authors |
0.41 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian-Cheng Luo | 1 | 99 | 20.75 |
| Qiuxiao Chen | 2 | 36 | 3.57 |
| j zheng | 3 | 2 | 0.41 |
| Yee Leung | 4 | 2081 | 96.44 |
| Jiang-Hong Ma | 5 | 99 | 12.21 |