Vertex component analysis: a fast algorithm to unmix hyperspectral data

• Published in: IEEE transactions on geoscience and remote sensing Vol. 43; no. 4; pp. 898 - 910
• Main Authors: Nascimento, J.M.P., Dias, J.M.B.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2005; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Computer simulation; Data mining; Extraction; Hyperspectral imaging; Hyperspectral sensors; Independent component analysis; Least squares approximation; Linear unmixing; Mathematical analysis; Pixel; Remote sensing; Scattering; simplex; Spectral lines; spectral mixture model; Spectral signatures; State of the art; Telecommunications; unmixing hypespectral data; unsupervised endmember extraction; Vectors (mathematics); vertex component analysis (VCA)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2005.844293

Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.