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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Nonnegative-Matrix-Factorization-Based Hyperspectral Unmixing With Partially Known Endmembers
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Nonnegative-Matrix-Factorization-Based Hyperspectral Unmixing With Partially Known Endmembers

机译：基于非负矩阵分解的高光谱与部分已知末端成员的混合

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摘要

Hyperspectral unmixing is an important technique for estimating fractions of various materials from remote sensing imagery. Most unmixing methods make the assumption that no prior knowledge of endmembers is available before the estimation. This is, however, not true for some unmixing tasks for which part of the endmember signatures may be known in advance. In this paper, we address the hyperspectral unmixing problem with partially known endmembers. We extend nonnegative-matrix-factorization-based unmixing algorithms to incorporate prior information into their models. The proposed approach uses the spectral signature of known endmembers as a constraint, among others, in the unmixing model, and propagates the knowledge by an optimization process which minimizes the difference between the image data and the prior knowledge. Results on both synthetic and real data have validated the effectiveness of the proposed method and have shown that it has outperformed several state-of-the-art methods that use or do not use prior knowledge of endmembers.
机译：高光谱分解是一种重要技术，可用于从遥感影像中估计各种物质的比例。大多数分解方法都假设在估计之前没有端成员的先验知识。但是，对于某些混合任务，情况并非如此，对于这些任务，可能会事先知道部分端成员签名。在本文中，我们使用部分已知的末端成员解决了高光谱分解问题。我们扩展了基于非负矩阵分解的混合算法，以将先验信息纳入其模型。所提出的方法在混合模型中使用了已知末端成员的光谱特征作为约束，并通过优化过程传播了知识，该过程使图像数据与现有知识之间的差异最小。综合数据和真实数据的结果均证实了该方法的有效性，并表明它优于使用或不使用末端成员先验知识的几种最新方法。

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