Given a spectral library, sparse unmixing aims to estimate the fractional proportions in each pixel of a hyperspectral image scene. However, the ever-growing dimensionality of spectral dictionaries strongly limits the performance of sparse unmixing algorithms. In this study, we propose a novel dictionary pruning (DP) approach to improve the performance of sparse unmixing algorithms, making them more accurate and time-efficient. We quantify the relative importance of each spectral dictionary atom using the total utility metric at virtually no cost. In this way, we have quantitative insights into how well the elements in the dictionary represent the hyperspectral scene. We evaluate the performance of the proposed dictionary pruning approach on several simulated data sets and one real data. We also compare the experimental results with two well-known dictionary pruning methods both visually and quantitatively and demonstrate the superiority of our proposed method through extensive experimental analysis.

中文翻译：

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用于稀疏高光谱解混的基于总效用度量的字典修剪

给定一个光谱库，稀疏解混合旨在估计高光谱图像场景的每个像素中的分数比例。然而，谱字典不断增长的维度严重限制了稀疏解混合算法的性能。在这项研究中，我们提出了一种新的字典剪枝 (DP) 方法来提高稀疏解混合算法的性能，使它们更准确和更省时。我们使用总效用指标以几乎免费的方式量化每个谱字典原子的相对重要性。通过这种方式，我们可以定量了解字典中的元素代表高光谱场景的程度。我们在几个模拟数据集和一个真实数据上评估了所提出的字典修剪方法的性能。