Semisupervised classification of SAR images by maximum margin neural networks method

Abstract

The proposed method is based on neural networks by modeling the data marginal distribution with the graph Laplacian built with both labeled and unlabeled samples, at the same time, optimizing neural networks layers in a single process, back-propagating the gradient of a Maximum Margin based objective function. Therefore, the proposed approach gives rise to an operational classifier, as opposed to previously presented semi-supervised scenarios. Results demonstrate the improved classification accuracy and scalability of this approach on SAR image classification problems.