Gaussian synapse ANNs in multi- and hyperspectral image data analysis

A new type of artificial neural network is used to identify different crops and ground elements from hyperspectral remote sensing data sets. These networks incorporate Gaussian synapses and are trained using a specific algorithm called Gaussian synapse back propagation described here. Gaussian synapses present an intrinsic filtering ability that permit concentrating on what is relevant in the spectra and automatically discard what is not. The networks are structurally adapted to the problem complexity as superfluous synapses and/or nodes are implicitly eliminated by the training procedure, thus pruning the network to the required size straight from the training set. The fundamental difference between the present proposal and other ANN topologies using Gaussian functions is that the latter use these functions as activation functions in the nodes, while in our case, they are used as synaptic elements, allowing them to be easily shaped during the training process to produce any type of n-dimensional discriminator. This paper proposes a multi- and hyperspectral image segmenter that results from the parallel and concurrent application of several of these networks providing a probability vector that is processed by a decision module. Depending on the criteria used for the decision module, different perspectives of the same image may be obtained. The resulting structure offers the possibility of resolving mixtures, that is, carrying out a spectral unmixing process in a very straightforward manner.