A new hyperspectral unmixing algorithm is proposed based on the normal compositional model (NCM) to estimate the endmembers and abundance parameters jointly in this paper. The NCM considers the hyperspectral imaging as a stochastic process and interprets each pixel value as a random vector, which is linearly mixed by the endmembers. More precisely, these endmembers are also treated as random variables as opposed to deterministic values in order to capture spectral variability that is not well described by the linear mixing model (LMM). However, the higher complexity of such an unmixing model leads to more difficulty in parameter estimation. A particle swarm optimization-expectation maximization (PSO-EM) algorithm, a "winner-take-all" version of the EM, is proposed to solve the parameter estimation problem, which employs a partial E step. The main contribution of the proposed PSO-EM is making optimum use of particle swarm optimization method (PSO) in the partial E step, which solves the difficulty of the integrals in the NCM model. The performance of the proposed methodology is evaluated through synthetic and real data experiments. Our obtained results demonstrate the superior performance of PSO-EM compared to other NCM-based as well as LMM-based methods.Index Terms-Expectation maximization (EM) algorithm, hyperspectral unmixing, normal compositional model (NCM), particle swarm optimization (PSO).
Hyperspectral remote sensing is a new and fast growing remote sensing technology that is currently being investigated by researchers and scientists. A great challenge in hyperspectral image analysis is decomposing a mixed pixel into a collection of endmembers and their corresponding abundance fractions, namely spectral unmixing. This paper introduces null subspace to the process of spectral unmixing. Null subspace is the orthogonal complement space of the subspace spanned by some endmembers. Take advantage of null subspace, this paper presents a solution of obtaining the distance from a pixel to subspace in the null subspace form. By analysis on the null subspace, all endmembers in the hyperspectral image can be extracted by the maximal distance criterion and the abundance can be obtained by the way of distance proportion. In the experiment, it shows that null subspace provides a fast and effective way for spectral unmixing.
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