Unsupervised classification of hyperspectral images using an Adaptive Vector Tunnel classifier

Hyperspectral image classification is one of the most popular information extraction methods in remote sensing applications. This method consists of variety of algorithms involving supervised, unsupervised or fuzzy classification, etc. In supervised classification, reference data which is known as a priori class information is used. On the other hand, computer based clustering algorithms are employed to group pixels which have similar spectral characteristics according to some statistical criteria in unsupervised classification. Among the most powerful techniques for hyperspectral image clustering, K-Means is one of the widely used iterative approaches. It is a simple though computationally expensive algorithm, particularly for clustering large hyperspectral images into many categories. During application of this technique, the Euclidian Distance (ED) measure is used to calculate the distances between pixel and local class centers. In this study, a new adaptive unsupervised classification technique is presented. It is a kind of vector tunnel around the randomly selected pixel spectra that changes according to spectral variation with respect to hyperspectral bands. Although vector tunnel classifier does not need training data or intensive mathematical calculation, classification results are comparable to K-Means Classification Algorithm.