论文标题
线性二次混合物的连续非负投影算法
Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures
论文作者
论文摘要
在这项工作中，我们通过脱离通常的线性模型来解决高光谱（HS）的问题，并专注于线性季度（LQ）。所提出的算法（称为线性二次混合物（SNPALQ））的连续非负投影算法扩展了连续的非负投影算法（SNPA），旨在解决线性模型下的未混合问题。通过沿SNPA方案的迭代局部固有的产品项显式建模，减轻混合中的非线性贡献，从而提高了分离质量。该方法在现实的数值实验中被证明是相关的。
In this work, we tackle the problem of hyperspectral (HS) unmixing by departing from the usual linear model and focusing on a Linear-Quadratic (LQ) one. The proposed algorithm, referred to as Successive Nonnegative Projection Algorithm for Linear Quadratic mixtures (SNPALQ), extends the Successive Nonnegative Projection Algorithm (SNPA), designed to address the unmixing problem under a linear model. By explicitly modeling the product terms inherent to the LQ model along the iterations of the SNPA scheme, the nonlinear contributions in the mixing are mitigated, thus improving the separation quality. The approach is shown to be relevant in a realistic numerical experiment.
