A Classification Algorithm for Hyperspectral Data based on Synergetics Theory

Abstract

This paper presents a classification methodology for hyperspectral data based on synergetics theory. Pattern recognition algorithms based on synergetics have been applied to images with limited success in the past, given their dependence on the rotation, shifting and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor. We introduce a representation in which each single spectrum, related to an image element in a hyperspectral scene, can be projected in a space spanned by a set of user-defined prototype vectors, which belong to some classes of interest. Each test vector is attracted by a final state associated to a prototype, and can thus be classified: this establishes a first attempt at performing a pixel-wise image classification using notions derived from synergetics. As typical synergetics-based systems have the drawback of a rigid training step, we introduce a new procedure which allows the selection of a training area for each class of interest, used to weight the prototype vectors through attention parameters and to produce a more accurate classification map through plurality vote of independent classifications. As each classification is in principle obtained on the basis of a single training sample per class, the proposed technique could be particularly effective in tasks where only a small training dataset is available. The results presented are promising and often outperform state of the art classification methodologies, both general and specific to hyperspectral data.