Image compression using a neural network

Data compression of speckled images poses a non-trivial model identification problem. We train an unsupervised neural network on a set of archetype images in order to form an internal representation (or model) of the image features. We find that a multi-layer topographic mapping network has the necessary properties successfully to compress and reconstruct imagery. We show how to extend and improve upon existing learning algorithms for this type of network, and we express the network learning dynamics as a diffusion equation. We then present some examples of the application of this technique to synthetic aperture radar images.