Liu Junmin, Zhang Chunxia, Zhang Jiangshe, Li Huirong, Gao Yuelin
School of Mathematics and Statistics, Xi'an Jiaotong University, Xianning West Road, Xi'an, 710049 China.
School of Information and Computing Science, Beifang University of Nationalities, Wenchang North Road, Yinchuan, 750021 China.
Springerplus. 2016 Nov 24;5(1):2007. doi: 10.1186/s40064-016-3671-6. eCollection 2016.
Recently, has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as , ignore the intrinsic geometric structure in the hyperspectral data.
In this paper, we propose a novel model, called , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on has been developed for the manifold regularized collaborative sparse regression model.
Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.
最近，[具体内容缺失]已成功应用于遥感高光谱图像的光谱混合分析。基于观测到的图像特征可以表示为预先已知的多个纯光谱特征的线性组合这一假设，场景中每个混合像元的分解是要在一个非常大的光谱库中找到特征的最优子集，这被转化为稀疏回归框架。然而，传统的稀疏回归模型，如[具体模型缺失]，忽略了高光谱数据中的内在几何结构。
在本文中，我们通过在协同稀疏回归模型中引入流形正则化，提出了一种名为[具体模型名称缺失]的新模型。流形正则化利用图拉普拉斯算子来纳入高光谱数据的局部几何结构。针对流形正则化协同稀疏回归模型，开发了一种基于[具体算法缺失]的算法。
在模拟和真实高光谱数据集上的实验结果证明了我们提出的模型的有效性。
