Application of the Constructive Mikado-Algorithm on Remotely Sensed Data

Finding an optimal architecture for a neural network, i.e. an optimal number and size of layers, is an open problem. With the Mikado-algorithm we present a new method to construct the network architecture in the course of learning. With two examples, classification and structure detection from remotely sensed data, we demonstrate the capabilities of the Mikado-algorithm. This algorithm provides good generalisation in the presence of mixed pixels, delivering small networks for high-dimensional problems and a new way of interpreting the network generalisation ability.