Nonlinear geometric manifold of hyperspectral data usually makes great trouble for accurate endmember extraction in literature. To address this issue, we propose a novel nonlinear endmember extraction algorithm by building a hypergraph and a fuzzy assessment strategy. The global change of nonlinear data manifold is first measured in a hypergraph whose hyperedges correspond to different local pixel subgroups. In contrast to edges in a simple graph, every hyperpath connected by multiple hyperedges instead of individual pixels effectively facilitates the determination of the simplex spanned by endmembers on complex data manifolds interfered by noises and outliers. Furthermore, in the hypergraph-based manifold system, a reliable fuzzy assessment mechanism for extracting the final endmembers is established by combining the classic simplex volume maximization rule with the inherent properties of hyperspectral data and hypergraph. In this procedure, the impact of outliers and the complex geometric structures of data can be further effectively reduced, leading to better unmixing results. Experimental results of various types of simulated data and real hyperspectral imagery demonstrate that the proposed algorithm (hypergraph and fuzzy-assessment-based nonlinear endmember extraction) can extract more accurate endmembers from complex data manifolds, and it is more robust to noises and outliers compared with state-of-the-art algorithms.

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通过基于超路径的单纯形生长和模糊评估的高光谱图像非线性端元识别

高光谱数据的非线性几何流形通常会给文献中的准确端元提取带来很大的麻烦。为了解决这个问题，我们通过构建超图和模糊评估策略提出了一种新的非线性端元提取算法。非线性数据流形的全局变化首先在超图中测量，其超边对应于不同的局部像素子组。与简单图中的边相比，由多个超边而不是单个像素连接的每个超路径有效地促进了由噪声和异常值干扰的复杂数据流形上的端元跨越的单纯形的确定。此外，在基于超图的流形系统中，通过将经典的单纯形体积最大化规则与高光谱数据和超图的固有特性相结合，建立了用于提取最终端元的可靠模糊评估机制。在此过程中，可以进一步有效降低异常值和数据复杂几何结构的影响，从而获得更好的解混结果。各种模拟数据和真实高光谱图像的实验结果表明，所提出的算法（基于超图和模糊评估的非线性端元提取）可以从复杂的数据流形中提取更准确的端元，并且与噪声和异常值相比，它对噪声和异常值的鲁棒性更强。最先进的算法。可以进一步有效降低异常值和数据复杂几何结构的影响，从而获得更好的解混结果。各种模拟数据和真实高光谱图像的实验结果表明，所提出的算法（基于超图和模糊评估的非线性端元提取）可以从复杂的数据流形中提取更准确的端元，并且与噪声和异常值相比，它对噪声和异常值的鲁棒性更强。最先进的算法。可以进一步有效降低异常值和数据复杂几何结构的影响，从而获得更好的解混结果。各种模拟数据和真实高光谱图像的实验结果表明，所提出的算法（基于超图和模糊评估的非线性端元提取）可以从复杂的数据流形中提取更准确的端元，并且与噪声和异常值相比，它对噪声和异常值的鲁棒性更强。最先进的算法。