Hyperspectral image classification by Multi-Scale Vector Tunnel

The spectral matching, statistical and kernel based methods are the most widely known classification algorithms for hyperspectral imaging. Spectral matching algorithms try to identify the similarity of the unknown spectral signature of test pixels with the expected signature. In this study, an efficient spectral similarity method employing Multi-Scale Vector Tunnel Algorithm (MS-VTA) for supervised classification of the materials in hyperspectral imagery is introduced. With the proposed algorithm, a simple spectral similarity based decision rule using some reference data or spectral signature is formed and compared with the Euclidian Distance (ED) and the Spectral Angle Map (SAM) classifiers. The prediction of multi-level upper and lower spectral boundaries of spectral signatures for all classes across spectral bands constitutes the basic principle of the proposed algorithm.