In hyperspectral sparse unmixing, a successful approach employs spectral bundles to address the variability of the endmembers in the spatial domain. However, the regularization penalties usually employed aggregate substantial computational complexity, and the solutions are very noise-sensitive. We generalize a multiscale spatial regularization approach to solve the unmixing problem by incorporating group sparsity-inducing mixed norms. Then, we propose a noise-robust method that can take advantage of the bundle structure to deal with endmember variability while ensuring inter- and intra-class sparsity in abundance estimation with reasonable computational cost. We also present a general heuristic to select the \emph{most representative} abundance estimation over multiple runs of the unmixing process, yielding a solution that is robust and highly reproducible. Experiments illustrate the robustness and consistency of the results when compared to related methods.

翻译：在高光谱稀疏解混中，一种有效的方法采用光谱束来处理空间域中端元的变异性。然而，常用的正则化惩罚项会显著增加计算复杂度，且解对噪声高度敏感。我们通过引入群稀疏诱导混合范数，推广了一种多尺度空间正则化方法以解决解混问题。进而提出一种鲁棒的噪声处理方法，该方法利用束结构处理端元变异性，同时以合理的计算成本确保丰度估计中的类间与类内稀疏性。我们还提出一种通用启发式方法，在多次解混过程中选取"最具代表性"的丰度估计，从而得到鲁棒且高度可重复的解。实验表明，与相关方法相比，本方法的结果具有鲁棒性和一致性。