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A sparsity promoting bilinear unmixing model

机译:稀疏促进双线性分解模型

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摘要

An algorithm, Bilinear SPICE (BISPICE), for simultaneously estimating the number of endmembers, the endmembers, and proportions for a bilinear mixing model is derived and evaluated. BISPICE generalizes the SPICE algorithm for linear mixing. The proportion estimation steps of SPICE and BISPICE are similar. However, the endmember updates, one novel aspect of the work, are quite different. The SPICE objective function is quadratic in the endmembers. The BISPICE is a fourth degree polynomial. In SPICE, endmembers are updated simultaneously via a closed form. In BISPICE, each endmember must be updated with respect to all other endmembers. Empirically, BISPICE estimated endmembers and proportions more accurately then SPICE, even though the data fitting error was higher.
机译:导出并评估了一种算法,即双线性SPICE(BISPICE),用于同时估计双线性混合模型的端成员数,端成员数和比例。 BISPICE概括了用于线性混合的SPICE算法。 SPICE和BISPICE的比例估计步骤相似。但是,最终成员更新是作品的一个新颖方面,却大不相同。在终端成员中,SPICE目标函数是二次方的。 BISPICE是四次多项式。在SPICE中,最终成员通过封闭形式同时更新。在BISPICE中,每个最终成员都必须相对于所有其他最终成员进行更新。根据经验,即使数据拟合误差较高,BISPICE仍比SPICE更准确地估计最终成员和比例。

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