ABSTRACT

A variety of unmixing methods offered fruitful solutions for extracting endmembers and estimating abundances. Recently, a matrix-vector nonnegative tensor factorization (MV-NTF) unmixing method was proposed. Compared with nonnegative matrix factorization (NMF), NTF avoids the conversion of hyperspectral data from 3-D to 2-D, thereby preserving the intrinsic structure information. Nevertheless, MV-NTF ignores local spatial information owing to dealing with data as a whole. Thus, in this letter, to make the most of spatial information and abundance sparsity, a new double weighted sparse NTF (DWSNTF) unmixing method is proposed. Under the MV-NTF framework, a double weighted $L_1$ regularizer is firstly utilized to characterize more precise and sparse abundance maps. One weight acts on single pixel to promote the sparsity of solution, while the other weight exploits the local spatial information to conserve more details and prevent oversmoothness. In addition, all weights are stacked into a weight tensor to fit the higher-dimensional factorization and facilitate optimization. Experimental results on both synthetic and real data demonstrate the validity and superiority of our proposed method against the state-of-the-art methods.

摘要

多种解混方法为提取末端成员和估计丰度提供了卓有成效的解决方案。最近，提出了矩阵矢量非负张量分解（MV-NTF）分解方法。与非负矩阵分解（NMF）相比，NTF避免了将高光谱数据从3-D转换为2-D，从而保留了固有结构信息。然而，由于整体上处理数据，MV-NTF忽略了局部空间信息。因此，在本文中，为了充分利用空间信息和稀疏度，提出了一种新的双重加权稀疏NTF（DWSNTF）分解方法。在MV-NTF框架下，双重加权${\mathrm{大号}}_{\mathrm{1个}}$正则化器首先用于表征更精确和稀疏的丰度图。一个权重作用在单个像素上以促进解决方案的稀疏性，而另一个权重则利用局部空间信息来保存更多细节并防止过度平滑。此外，所有权重都堆叠到权重张量中，以适应更高维度的因式分解并有助于优化。综合和真实数据的实验结果证明了我们提出的方法相对于最新方法的有效性和优越性。