New developments on VCA unmixing algorithm

José M. P. Nascimento; José M. Bioucas-Dias

doi:10.1117/12.799838

10 October 2008 New developments on VCA unmixing algorithm

José M. P. Nascimento, José M. Bioucas-Dias

Proceedings Volume 7109, Image and Signal Processing for Remote Sensing XIV; 71090F (2008) https://doi.org/10.1117/12.799838
Event: SPIE Remote Sensing, 2008, Cardiff, Wales, United Kingdom

Abstract

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 efficient 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.

Citation Download Citation

José M. P. Nascimento and José M. Bioucas-Dias "New developments on VCA unmixing algorithm", Proc. SPIE 7109, Image and Signal Processing for Remote Sensing XIV, 71090F (10 October 2008); https://doi.org/10.1117/12.799838

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