Spatial resolution improvement of remotely sensed images by a fully interconnected neural network approach

In previous works, backpropagation neural networks (BPNN) had been applied successfully in the spatial resolution improvement of remotely sensed, low-resolution images using data fusion techniques. However, the time required in the learning stage is long. In the present paper, a fully interconnected neural network (NN) model, valid from the mathematical and neurobiological points of view, is developed. With this model, the global minimum error is reached considerably faster than with any other method without regarding the initial settings of the network parameters.