ABSTRACT Sparse spectral unmixing assumes the constituent spectra (endmembers) can be selected from a given spectral library and seeks sparse combination coefficients (abundances) of endmembers to reconstruct the input hyperspectral image. The existing methods extensively exploit the prior information of abundances and design various regularizations on abundances. In this paper, taking advantage of the low-rank property of the reconstructed image, we propose a generic unmixing model by incorporating a low-rank regularization on the data reconstruction term to the traditional sparse unmixing models. The low-rank reconstruction regularization plays a role in attenuating noise and errors, therefore helps our models achieve better sparse recovery results while preserving details. We illustrate two models which combine the low-rank reconstruction term with sparse and joint sparse abundances regularizers, respectively. A series of simulation and real-world data are used to evaluate the performances of the proposed models, and their results are compared with that obtained by state-of-the-art algorithms. Both visual comparison and quantitative evaluation are presented to show the effectiveness of our methods.

中文翻译：

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用于高光谱解混的低秩重建稀疏丰度估计

摘要稀疏光谱解混假设可以从给定的光谱库中选择组成光谱（端元），并寻找端元的稀疏组合系数（丰度）来重建输入的高光谱图像。现有方法广泛利用丰度的先验信息并设计丰度的各种正则化。在本文中，利用重建图像的低秩特性，我们通过将数据重建项的低秩正则化合并到传统的稀疏分离模型中，提出了一种通用的分离模型。低秩重建正则化在衰减噪声和错误方面发挥作用，因此有助于我们的模型在保留细节的同时获得更好的稀疏恢复结果。我们说明了两个模型，它们分别将低秩重建项与稀疏和联合稀疏丰度正则化器相结合。一系列模拟和真实世界数据用于评估所提出模型的性能，并将其结果与通过最先进算法获得的结果进行比较。提供了视觉比较和定量评估以显示我们方法的有效性。