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Bilinear mixture models based unsupervised nonlinear unmixing using constrained nonnegative matrix factorization

Nonnegative matrix factorization (NMF) is often used for unsupervised spectral unmixing in recent years. In this paper, a constrained NMF algorithm based on the bilinear mixture models for unsupervised nonlinear spectral unmixing is proposed. By using a distance measure without dimension reduction, data's projection on a group of constructed hyperplanes representing the nonlinearity are obtained so that the linear parts of data can be approximately determined. Further, we adopt NMF incorporated with a minimum distance constraint for unmixing with the hyperplanes being reconstructed repeatedly during the iteration. Experimental results on synthetic and real hyperspectral data indicate that the proposed algorithm has good unmixing performance.