Unsupervised classification of polarimetric SAR images using neural networks

We study two unsupervised algorithms for polarimetric SAR image classification. The first one is Cloude´s decomposition algorithm. The main advantage of this unsupervised algorithm is to provide terrain identification information where the most important kinds of scattering medium can be discriminated. However, his main advantage is the arbitrary location of decision boundaries. To surmount this insufficiency, we present the second algorithm based on neural networks. We propose a new scheme of unsupervised classification that combine the most important kind of trained nets.