A Classification Algorithm for Hyperspectral Data Based on Synergetics Theory

Abstract

ABSTRACT:This paper presents a new classification methodology for hyperspectral data based on synergetics theory, which describes the spontaneous formation of patterns and structures in a system through self-organization. We introduce a representation for hyperspectral data, in which a spectrum 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 be thus classified. As typical synergetics-based systems have the drawback of a rigid training step, we modify it to allow the selection of user-defined training areas, used to weight the prototype vectors through attention parameters and to produce a more accurate classification map through majority voting of independent classifications. Results are comparable to state of the art classification methodologies, both general and specific to hyperspectral data and, as each classification is based on a single training sample per class, the proposed technique would be particularly effective in tasks where only a small training dataset is available.

Discrimination of Tropical Mangroves at the Species Level with EO-1 Hyperion Data

“…Lastly, the whole process was started over again as the new generation chromosomes were tested for their fitness scores. The spectral angle mapper classifier (SAM) is one of the most popular classification techniques for hyperspectral data [83,84]. First, the reflectance of each pixel is coded as n-dimensional vectors.…”