New Developments on VCA Unmixing Algorithm
New Developments on VCA Unmixing Algorithm , Proc SPIE - Conf. on Image and Signal Processing for Remote Sensing, Cardiff, United Kingdom, Vol. 7109, pp. 71090F - 71090F-9, September, 2008.
Digital Object Identifier: 10.1117/12.799838
Hyperspectral sensors are being developed for remote sensing applications. These sensors produce huge data
volumes which require faster processing and analysis tools. Vertex component analysis (VCA) has become a
very useful tool to unmix hyperspectral data. It has been successfully used to determine endmembers and unmix
large hyperspectral data sets without the use of any a priori knowledge of the constituent spectra. Compared
with other geometric-based approaches VCA is an e±cient method from the computational point of view.
In this paper we introduce new developments for VCA: 1) a new signal subspace identification method (HySime) is applied to infer the signal subspace where the data set live. This step also infers the number of endmembers present in the data set; 2) after the projection of the data set onto the signal subspace, the algorithm
iteratively projects the data set onto several directions orthogonal to the subspace spanned by the endmembers
already determined. The new endmember signature corresponds to these extreme of the projections. The
capability of VCA to unmix large hyperspectral scenes (real or simulated), with low computational complexity,
is also illustrated.