Sparse unmixing (SU) can represent an observed image using pure spectral signatures and corresponding fractional abundance from a large spectral library and is an important technique in hyperspectral unmixing. However, the existing SU algorithms mainly exploit spatial information from a fixed neighborhood system, which is not sufficient. To solve this problem, we propose a nonlocal weighted SU algorithm based on global search (G-NLWSU). By exploring the nonlocal similarity of the hyperspectral image, the weights of pixels are calculated to form a matrix to weight the abundance matrix. Specifically, G-NLWSU first searches for a similar group of each pixel in the global scope then uses singular value decomposition to denoise and finally obtains the weight matrix by considering correlations between similar pixels. To reduce the execution burden of G-NLWSU, we propose a parallel computing version of G-NLWSU, named PG-NLWSU, which integrates compute unified device architecture-based parallel computing into G-NLWSU to accelerate the search for groups of nonlocally similar pixels. Our proposed algorithms shed new light on SU by considering a new exploitation process of spatial information and parallel computing scenario. Experimental results conducted on simulated and real datasets show that PG-NLWSU is superior to comparison algorithms.

中文翻译：

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基于全局搜索和并行优化的非局部加权稀疏分解

稀疏分解（SU）可以使用纯光谱特征和来自大型光谱库的相应分数丰度表示观察到的图像，并且是高光谱分解中的一项重要技术。然而，现有的SU算法主要利用来自固定邻域系统的空间信息，这是不够的。为了解决这个问题，我们提出了一种基于全局搜索的非局部加权SU算法（G-NLWSU）。通过探索高光谱图像的非局部相似性，计算像素的权重以形成矩阵以对丰度矩阵进行加权。具体而言，G-NLWSU首先在全局范围内搜索每个像素的相似组，然后使用奇异值分解进行降噪，最后通过考虑相似像素之间的相关性获得权重矩阵。为了减轻G-NLWSU的执行负担，我们提出了G-NLWSU的并行计算版本，称为PG-NLWSU，它将基于计算统一设备体系结构的并行计算集成到G-NLWSU中，以加快对非局部相似像素组的搜索。 。通过考虑空间信息和并行计算场景的新开发过程，我们提出的算法为SU提供了新的思路。在模拟和真实数据集上进行的实验结果表明，PG-NLWSU优于比较算法。通过考虑空间信息和并行计算场景的新开发过程，我们提出的算法为SU提供了新的思路。在模拟和真实数据集上进行的实验结果表明，PG-NLWSU优于比较算法。通过考虑空间信息和并行计算场景的新开发过程，我们提出的算法为SU提供了新的思路。在模拟和真实数据集上进行的实验结果表明，PG-NLWSU优于比较算法。