SAR Imagery Classification in Extended Feature Space by Collective Network of Binary Classifiers
Polari metric SAR image classification has been an active research field where several features and classifiers have been proposed in the past. Using numerous features can be a desirable option so as to achieve a better discrimination over certain classes, yet key questions such as how to avoid "Curse of Dimensionality" and how to combine them in the most effective way still remains unanswered. In this paper, the authors investigate SAR image classification using a large set of features, where the focus is particularly drawn on the extension of image processing features e.g. texture, edge and color. They propose a dedicated application of the Collective Network of (evolutionary) Binary Classifiers (CNBC) framework to address these problems with the aim of achieving high feature scalability.