Image classification in remote sensing using functional link neural networks

A new objective function for functional link net classifier design is presented, which has more free parameters than the classical objective function. An iterative minimization technique for the objective function is described which requires the solution of multiple sets of numerically ill conditioned linear equations. A numerically stable solution to the functional link neural network design equations, which utilizes the conjugate gradient algorithm, is presented. The design method is applied to networks used to classify SAR imagery from remote sensing. The functional link discriminants are seen to outperform Bayes-Gaussian discriminants in the examples.<>