In this paper, we propose a new algorithm integrating pure pixel identification into nonnegative matrix factorization (NMF) model to decompose the mixed pixels existing in hyperspectral imagery. The proposed algorithm employs traditional endmember identification algorithm to search for the pure pixel candidates, and then the principal component analysis is performed on the homogenous pixels which consist of the pure pixel candidates and its neighborhoods to identify the endmembers existing in the real scene. Finally, the known-endmember-based NMF unmixing algorithm is used to generate the other unknown endmembers. The proposed algorithm retains the advantages of both pure pixel identification method and NMF. Experimental results based on simulated and real data sets demonstrate the superiority of the proposed algorithm with respect to other state-of-the-art approaches.
Spectral mixture analysis (also called spectral unmixing) is one of the important and effective techniques to estimate abundance fractions of materials present in the hyperspectral imagery. Linear spectral mixture modeling is widely used in solving the spectral unmixing problems as its conciseness and clarity of physical meaning. This paper presents a novel algorithm to produce fully constrained (i.e. non-negative and sum to one constrained) abundance using the barycentric coordinates in the n-simplex. To impose non-negative constraint on the abundance, the proposed method use a serious of orthogonal projections to find the fully constrained solution, which takes into account the geometric structure of hyperspectral data set. The proposed algorithm is in line with the least squares criterion. The efficiency and effectiveness of the resulting unmixing algorithm is demonstrated using both synthetic and real hyperspectral images.
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