Non-Local Sparse Unmixing for Hyperspectral Remote Sensing Imagery
Non-Local Sparse Unmixing for Hyperspectral Remote Sensing Imagery
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DOI：
10.1109/jstars.2013.2280063
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发表时间：
2014-06
影响因子：
5.5
通讯作者：
Yanfei Zhong;Ruyi Feng;Liangpei Zhang
中科院分区：
文献类型：
--
作者：
Yanfei Zhong;Ruyi Feng;Liangpei Zhang
稀疏解混是一种很有前途的方法，它通过假设所观察到的图像签名可以以预先已知的多个纯光谱签名的线性组合的形式表示来充当半监督解混策略。然而，传统的稀疏解混涉及到在一个非常大的标准光谱库中为观测数据找到最佳的签名子集，而不考虑空间信息。提出了一种基于非局部均值的高光谱遥感图像稀疏解混算法--非局部稀疏解混算法（NLSU）。在NLSU中，非局部均值方法，作为稀疏分解的正则化器，被用来利用丰度图像中的相似模式和结构。基于稀疏光谱解混模型的NLSU算法通过对丰度图像中所有像素进行加权平均，融合非局部空间信息，提高了光谱解混精度。五个实验与三个模拟和两个真实的高光谱图像进行评估的性能相比，以前的稀疏解混方法：稀疏解混通过变量分裂和增广拉格朗日（SUnSAL）和稀疏解混通过变量分裂增广拉格朗日和全变分（SUnSAL-TV）。实验结果表明，NLSU优于其他算法，具有更好的光谱分解精度，是一种有效的高光谱遥感图像光谱分解算法。
Sparse unmixing is a promising approach that acts as a semi-supervised unmixing strategy by assuming that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures that are known in advance. However, conventional sparse unmixing involves finding the optimal subset of signatures for the observed data in a very large standard spectral library, without considering the spatial information. In this paper, a new sparse unmixing algorithm based on non-local means, namely non-local sparse unmixing (NLSU), is proposed to perform the unmixing task for hyperspectral remote sensing imagery. In NLSU, the non-local means method, as a regularizer for sparse unmixing, is used to exploit the similar patterns and structures in the abundance image. The NLSU algorithm based on the sparse spectral unmixing model can improve the spectral unmixing accuracy by incorporating the non-local spatial information by means of a weighting average for all the pixels in the abundance image. Five experiments with three simulated and two real hyperspectral images were performed to evaluate the performance of the proposed algorithm in comparison to the previous sparse unmixing methods: sparse unmixing via variable splitting and augmented Lagrangian (SUnSAL) and sparse unmixing via variable splitting augmented Lagrangian and total variation (SUnSAL-TV). The experimental results demonstrate that NLSU outperforms the other algorithms, with a better spectral unmixing accuracy, and is an effective spectral unmixing algorithm for hyperspectral remote sensing imagery.