A uniform projection-based unsupervised classification for hyperspectral imagery

Orthogonal subspace projection (OSP) has received considerable interest in hyperspectral image classification. It performs well when the complete knowledge of image endmembers is available. In reality, obtaining such prior knowledge is very difficult, if not impossible. This work investigates a variant of OSP, called uniform projection (UP) and presents a UP-based unsupervised algorithm for hyperspectral image classification. It first implements a uniform target detector (UTD) to find a set of interfering signatures, I, then another uniform I-annihilated detector (UIAD) to find desired target signatures. The signatures in I are combined with the UIAD found desired signatures to form a target signatures matrix M which can be used for supervised image classification. In this case, OSP can be used for this purpose. In order to demonstrate the utility of the proposed unsupervised classification technique for hyperspectral imagery, a series of computer simulations and experiments are conducted for analysis.