Progressive Band Selection of Spectral Unmixing for Hyperspectral Imagery

Abstract

A new band selection (BS), called progressive BS (PBS) of spectral unmixing for hyperspectral imagery is being presented. It is quite different from the traditional BS in the sense that the former adapts the number of selected bands, p to various endmembers used for spectral unmixing, while the latter fixes the value of p at a constant for all endmembers. Due to the fact that different endmembers post various levels of difficulty in discrimination, each endmember should have its own custom-selected bands to specify its spectral characteristics. In order to address this issue, p is composed of two values, one value determined by virtual dimensionality to accommodate each of endmembers and the other is determined by a new concept of band dimensionality allocation to account for discrminability among endmembers. In order to find appropriate bands to be used for PBS, band prioritization and band de-correlation are included to rank bands according to significance of band information and to remove interband redundancy, respectively. As a result, spectral unmixing can be performed progressively by selecting different bands for various endmembers, a task that the traditional BS cannot accomplish. The effectiveness and advantages of using PBS over BS are also demonstrated by experiments.