SAR imagery classification in extended feature space by Collective Network of Binary Classifiers

Polarimetric 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, we 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. We 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. We furthermore tested several SAR and image processing feature constellations over three well-known SAR image classifiers and make comparative evaluations with CNBC. Experimental results over the full polarimetric AIRSAR San Francisco Bay and Flevoland images show that additional image processing features are able to improve SAR image classification accuracy and moreover, the CNBC proves useful and can scale well especially whenever high number of features and classes are encountered.