论文标题
通过非负基质分解的高光谱脉络用手工和学先的先验分解
Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learnt Priors
论文作者
论文摘要
如今，基于非负矩阵分解（NMF）方法已广泛应用于盲光频谱不混合。将适当的正规化器引入NMF对于数学上约束解决方案并实际利用图像的光谱和空间特性至关重要。通常，正确手工制作正规化器并解决相关的复杂优化问题是非平凡的任务。在我们的工作中，我们提出了一个基于NMF的Unmixing框架，该框架共同使用手工制作的正规器和从数据中学到的正规器。我们插入了丰度的先验，可以使用各种图像Denoisiser来解决相关的子问题，我们考虑到丰度矩阵的L_2,1-摩尔正规剂，以促进稀疏的Unmixing结果。提出的框架是灵活的和可扩展的。综合数据和实际空气传播数据均可确认我们方法的有效性。
Nowadays, nonnegative matrix factorization (NMF) based methods have been widely applied to blind spectral unmixing. Introducing proper regularizers to NMF is crucial for mathematically constraining the solutions and physically exploiting spectral and spatial properties of images. Generally, properly handcrafting regularizers and solving the associated complex optimization problem are non-trivial tasks. In our work, we propose an NMF based unmixing framework which jointly uses a handcrafting regularizer and a learnt regularizer from data. we plug learnt priors of abundances where the associated subproblem can be addressed using various image denoisers, and we consider an l_2,1-norm regularizer to the abundance matrix to promote sparse unmixing results. The proposed framework is flexible and extendable. Both synthetic data and real airborne data are conducted to confirm the effectiveness of our method.
