Spectral Unmixing via Data-Guided Sparsity
Spectral Unmixing via Data-Guided Sparsity
复制标题
DOI：
10.1109/tip.2014.2363423
复制
发表时间：
2014-03
影响因子：
10.6
通讯作者：
Feiyun Zhu;Ying Wang;Bin Fan;Shiming Xiang;Gaofeng Meng;Chunhong Pan
中科院分区：
文献类型：
--
作者：
Feiyun Zhu;Ying Wang;Bin Fan;Shiming Xiang;Gaofeng Meng;Chunhong Pan
高光谱解混是一项重要的任务，它是在高光谱分析、可视化和理解过程中，估计一组共同的光谱基及其相应的合成百分比。从无监督学习的角度来看，这个问题是非常具有挑战性的-频谱基础和它们的复合百分比都是未知的，使得解决方案空间太大。为了减少解空间，已经提出了许多方法，通过利用各种先验知识。在实践中，这些先验知识很容易导致一些不合适的解决方案。这是因为它们是通过对所有因素施加相同的约束强度来实现的，而这在实践中并不成立。为了克服这一局限性，我们提出了一种新的基于稀疏性的方法，通过学习数据引导图（DgMap）来描述每个像素的单独混合水平。通过该DgMap，以自适应方式应用了Dgp（0 <; p <; 1）约束。这种实现方式不仅符合实际情况，而且在高稀疏性约束下引导谱基向像素方向移动。此外，本文还提出了一种简洁的优化方案，并证明了其收敛性。在多个数据集上的实验也证明了DgMap方法的可行性，并得到了高质量的混合图像分解结果。
Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral analysis, visualization, and understanding. From an unsupervised learning perspective, this problem is very challenging-both the spectral bases and their composite percentages are unknown, making the solution space too large. To reduce the solution space, many approaches have been proposed by exploiting various priors. In practice, these priors would easily lead to some unsuitable solution. This is because they are achieved by applying an identical strength of constraints to all the factors, which does not hold in practice. To overcome this limitation, we propose a novel sparsity-based method by learning a data-guided map (DgMap) to describe the individual mixed level of each pixel. Through this DgMap, the ℓp (0 <; p <; 1) constraint is applied in an adaptive manner. Such implementation not only meets the practical situation, but also guides the spectral bases toward the pixels under highly sparse constraint. What is more, an elegant optimization scheme as well as its convergence proof have been provided in this paper. Extensive experiments on several datasets also demonstrate that the DgMap is feasible, and high quality unmixing results could be obtained by our method.