Multi-scale vector tunnel classification algorithm for hyperspectral images

Hyperspectral image (HSI) classification consists of a variety of algorithms involving supervised or unsupervised. In supervised classification, some reference data are used. Training data are not used in unsupervised classification methods. The type of a classification algorithm depends on the nature of the input and reference data. 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. Even though most spectra in real applications are random, the amount of training data with respect to the dimensionality affects the performances of the statistical classifiers substantially. 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 limited amount of 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.