Abstract
Virtual dimensionality (VD) is a new concept which was originally developed for estimating the number of spectrally distinct signatures present in hyperspectral data. The effectiveness of the VD is determined by the technique used for VD estimation. This paper develops an orthogonal subspace projection (OSP) technique to estimate the VD. The idea is derived from linear spectral mixture analysis where a data sample vector is modeled as a linear mixture of a finite set of what is called as virtual endmembers in this paper. A similar idea was also previously investigated by the signal subspace estimate (SSE) and was later improved by hyperspectral signal subspace identification by minimum error (HySime), where the minimum mean squared error is used as a criterion to determine the VD. Interestingly, with an appropriate interpretation, the proposed OSP technique includes the SSE/HySime as its special case. In order to demonstrate its utility, experiments using synthetic images and real image data sets are conducted for performance analysis.
Original language | English |
---|---|
Article number | 5595092 |
Pages (from-to) | 3960-3979 |
Number of pages | 20 |
Journal | IEEE Transactions on Geoscience and Remote Sensing |
Volume | 48 |
Issue number | 11 |
DOIs | |
Publication status | Published - 2010 Nov |
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- General Earth and Planetary Sciences