Projection-Based NMF for Hyperspectral Unmixing

As a widely concerned research topic, many advanced algorithms have been proposed for hyperspectral unmixing. However, they may fail to accurately identify endmember signatures when coming across insufficient spatial resolution. To deal with this problem, an algorithm based on semisupervised linear sparse regression is proposed, in which unmixing procedure is reduced to seeking an optimal subset from the spectral library to best model mixed pixels in the scene. However, the number of the spectra with nonzero abundance is much more than that of the true endmember signatures. Furthermore, the selection of library spectra as endmember signatures is undesirable due to the divergent imaging conditions. In this paper, a novel textit{projection-based nonnegative matrix factorization} (NMF) (PNMF) algorithm is proposed by importing spectra library into the NMF framework. The main novelties of this paper are listed as follows. 1) By introducing the spectral library, the extraction of endmember signatures is no longer restricted by spatial resolution. 2) Related spectra are selected and projected onto a subspace containing the endmember signatures. So that the number of endmember signatures is controlled by dimension of the subspace. 3) In PNMF, the endmember signatures are adaptively generated from the spectral library, and are matched with the observed hyperspectral images. This overcomes the difficulty caused by diverse imaging conditions, and makes the proposed algorithm more practical for real applications. The experimental results, conducted on both synthetic and real hyperspectral data, illustrate the advantages of the proposed algorithm when compared with the state-of-the-art algorithms.