Orthogonal subspace projection approach to finding signal sources in hyperspectral imagery

Abstract

The usefulness of orthogonal subspace projection (OSP) has been demonstrated in many applications. Automatic Target Generation Process (ATGP) was previously developed for automatic target recognition for Hyperspectral imagery by implementing a successive OSP. However, ATGP itself does not provide a stopping rule to determine how many signal sources present and need to be extracted in the image. This paper presents a new application of ATGP in determining the number of signal sources and finding these signal sources in the image at the same time. The idea is to categorize signal sources into target classes and background classes in terms of their inter-sample spectral correlation (ISSC). Two separate algorithms, unsupervised target sample generation (UTSG) and unsupervised background sample generation (UBSG) are developed for this purpose. The UTSG implements a sequence of successive OSP in the sphered hyperspectral data to determine the number of target signal sources whose ISSC are characterized by high order statistics (HOS) and find the target signal sources to at the same time. It is then followed by the UBSG which operates the ATGP on a space orthogonal to the subspace generated by the target samples to determine and find background signal sources. Both UTSG and UBSG are terminated by an effective stopping rule which can be used to estimate the virtual dimensionality (VD). Two data sets, synthetic image data and real image scenes are used for experiments. Experimental results demonstrate that the UTSG and UBSG are effective in extracting signal sources in various applications.