Structured Sparse Method for Hyperspectral Unmixing
详细信息 查看全文
- 作者：Feiyun Zhu ; Ying Wang ; Shiming Xiang ; Bin Fan ; Chunhong Pan
- 关键词：Hyperspectral Unmixing (HU) ; Hyperspectral image analysis ; Structured Sparse NMF (SS-NMF) ; Mixed pixel ; Nonnegative Matrix Factorization (NMF)
- 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
- 出版年：February, 2014
- 年：2014
- 卷：88
- 期：Complete
- 页码：101-118
- 全文大小：10734 K
文摘
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.