提出新型两步线性混合模型，用二阶优化解决光谱解混中的端元变化问题。
A Tractable Two-Step Linear Mixing Model Solved with Second-Order Optimization for Spectral Unmixing under Variability
- 分图像级与像素级两次缩放，降低模型复杂度
- 二阶优化求解近似凸问题，解混精度优于现有方法
- 无需调参，适合各类解混任务，尤其擅长盲解混
本文提出一种两步线性混合模型（2LMM），在模型复杂度与计算可 tractability 之间取得平衡。该模型引入两个独立的缩放步骤：跨图像的端元缩放和像素级缩放。我们证明该模型仅导致轻微非凸优化问题，并采用包含二阶信息的优化算法求解。据作者所知，这是首次将二阶优化技术应用于建模端元变异的光谱解混问题。该方法高度鲁棒，几乎无需超参数调整，可快速应用于多种解混任务。通过在模拟和真实数据上的大量实验验证，新模型在解混性能上具有竞争力，某些情况下甚至优于当前最优方法，且在盲解混等挑战性场景中表现优异。
原文摘要 · Abstract (English)
In this paper, we propose a Two-Step Linear Mixing Model (2LMM) that bridges the gap between model complexity and computational tractability. The model achieves this by introducing two distinct scaling steps: an endmember scaling step across the image, and another for pixel-wise scaling. We show that this model leads to only a mildly non-convex optimization problem, which we solve with an optimization algorithm that incorporates second-order information. To the authors' knowledge, this work represents the first application of second-order optimization techniques to solve a spectral unmixing problem that models endmember variability. Our method is highly robust, as it requires virtually no hyperparameter tuning and can therefore be used easily and quickly in a wide range of unmixing tasks. We show through extensive experiments on both simulated and real data that the new model is competitive and in some cases superior to the state of the art in unmixing. The model also performs very well in challenging scenarios, such as blind unmixing.
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