An elliptical basis function (EBF) network is proposed 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 uses 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 proposed 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

which leas to more reasonable classification. Experimental results show that

compared to RBF network

the EM-based EBF network is more accurate and simpler in structure.