Abstract-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. Index Terms-Linear unmixing, simplex, spectral mixture model, unmixing hypespectral data, unsupervised endmember extraction, vertex component analysis (VCA).
Extending subspace approach: A fast unmixing algorithm, termed vertex component analysis (VCA), is described in REF .
Vertex component analysis: a fast algorithm to unmix hyperspectral data
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing
Computer Science Mathematics