用最优传输方法提升高混合光谱解混精度
Optimal Transport Based Hyperspectral Unmixing for Highly Mixed Observations
- 通过最优传输度量丰度分布差异并作为正则化项
- 在高混合数据下更准确估计端元成分
- 对目标分布选择不敏感，适合实际应用
我们提出一种基于最优传输（OT）的新方法，用于解决盲光谱解混中的高度混合数据问题。该方法将估计的丰度矩阵分布约束得更接近目标狄利克雷分布，创新之处在于使用最优传输来衡量目标与真实丰度分布之间的差异，并将其作为优化问题中的正则项。通过无监督深度学习的案例研究，验证了该方法的有效性。实验表明，该方法在高度混合数据下能更准确地估计端元成分，且对目标丰度分布的选择具有鲁棒性。
原文摘要 · Abstract (English)
We propose a novel approach based on optimal transport (OT) for tackling the problem of highly mixed data in blind hyperspectral unmixing. Our method constrains the distribution of the estimated abundance matrix to resemble a targeted Dirichlet distribution more closely. The novelty lies in using OT to measure the discrepancy between the targeted and true abundance distributions, which we incorporate as a regularization term in our optimization problem. We demonstrate the efficiency of our method through a case study involving an unsupervised deep learning approach. Our experiments show that the proposed approach allows for a better estimation of the endmembers in the presence of highly mixed data, while displaying robustness to the choice of target abundance distribution.
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