Structured Sparse Method for Hyperspectral Unmixing
Structured Sparse Method for Hyperspectral Unmixing
复制标题
高光谱解混的结构化稀疏方法
DOI：
10.1016/j.isprsjprs.2013.11.014
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发表时间：
2014
影响因子：
12.7
通讯作者：
Pan Chunhong
中科院分区：
文献类型：
--
作者：
Zhu Feiyun;Wang Ying;Xiang Shiming;Fan Bin;Pan Chunhong
高光谱解混（HU）由于能够揭示高光谱数据中隐藏的信息，在过去的几十年里受到了越来越多的关注。遗憾的是，大多数现有的方法未能利用数据中的空间信息。为了克服这个局限性，我们提出了一个结构稀疏正则非负矩阵分解（SS-NMF）方法基于以下两个方面。首先，我们将一个图形拉普拉斯编码嵌入在高光谱数据空间的流形结构。以这种方式，高度相似的相邻像素可以被分组在一起。其次，套索惩罚被用于SS-NMF中，因为相同流形结构中的像素被一组公共的相关基稀疏混合。这两个因素作为一个新的结构化稀疏约束。有了这个约束，我们的方法可以学习一个紧凑的空间，其中高度相似的像素被分组以共享相关的稀疏表示。在不同噪声水平的真实的高光谱数据集上的实验表明，该方法的性能明显优于现有的方法。
Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.