Orthogonal Subspace Projection Approach to Finding Signal Sourcesin Hyperspectral Imagery

摘要

The usefulness of orthogonal subspace projection (OSP) has been demonstrated in many applications. Automatic TargetGeneration Process (ATGP) was previously developed for automatic target recognition for Hyperspectral imagery byimplementing a successive OSP. However, ATGP itself does not provide a stopping rule to determine how many signalsources present and need to be extracted in the image. This paper presents a new application of ATGP in determining thenumber of signal sources and finding these signal sources in the image at the same time. The idea is to categorize signalsources into target classes and background classes in terms of their inter-sample spectral correlation (ISSC). Twoseparate 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 spheredhyperspectral data to determine the number of target signal sources whose ISSC are characterized by high order statistics(Н05) and find the target signal sources to at the same time. It is then followed by the UBSG which operates the ATGPon 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 virtualdimensionality (VD). Two data sets, synthetic image data and real image scenes are used for experiments. Experimentalresults demonstrate that the UTSG and UBSG are effective in extracting signal sources in various applications.