Endmember-specified virtual dimensionality in hyperspectral imagery

One key issue encountered in endmember extraction is to determine the number of endmembers, p, required to be extracted. Virtual dimensionality (VD) has been widely used for this purpose. However, VD was originally developed and defined as the number of spectrally distinct signatures which are not necessarily pure signatures. So, on some occasions the VD estimated value for p may not be accurate to be used for the number of endmembers. This paper develops an endmember-specified VD (ES-VD) which makes use of data sample vectors generated by a specific endemember finding algorithm (EFA) as target signal sources and then determine if these signal sources are indeed true endmember by a binary composite hypothesis testing to determine endmembers. As a result, ES-VD varies with different target signal sources produced by EFAs. Most importantly, ES-VD not only determines the value of VD and in the mean time it also finds desired endmembers.