Tensor-Based Low-Rank Graph With Multimanifold Regularization for Dimensionality Reduction of Hyperspectral Images
Tensor-Based Low-Rank Graph With Multimanifold Regularization for Dimensionality Reduction of Hyperspectral Images
复制标题
基于张量的低秩图与多流形正则化用于高光谱图像的降维
DOI：
10.1109/tgrs.2018.2835514
复制
发表时间：
2018-08-01
影响因子：
8.2
通讯作者：
Jiao, Licheng
中科院分区：
文献类型：
--
作者：
An, Jinliang;Zhang, Xiangrong;Jiao, Licheng
高光谱图像处理中的一个重要任务是去噪。如何在保持原有结构信息的同时，提高判别能力，仍然是该领域的一个挑战。近年来，低秩表示由于具有保持全局内在结构信息的优点而被应用于数据降维，并取得了良好的效果。通过利用原始数据集的子流形信息，多流形学习可以有效地提高处理后数据集的判别能力。另外，由于张量分析具有保持空间邻域结构信息的能力，张量分析已成为高光谱图像处理的一种流行技术。基于上述分析，本文提出了一种新的基于张量的低秩图多流形正则化（T-LGMR）的高光谱图像降维方法。在T-LGMR中，低秩约束用于保持全局数据结构，而多流形信息用于增强判别能力，并且张量表示用于保持空间邻域信息。最后，在图嵌入框架中实现降维。三个真实的高光谱数据集上的实验结果表明，该方法优于几个国家的最先进的方法。
Dimensionality reduction is an essential task in hyperspectral image processing. How to preserve the original intrinsic structure information and enhance the discriminant ability is still a challenge in this area. Recently, with the advantage of preserving global intrinsic structure information, low-rank representation has been applied to dimensionality reduction and achieved promising performance. By exploiting the submanifold information of the original data set, multimanifold learning is effective in enhancing the discriminant ability of the processed data set. In addition, due to the ability of preserving the spatial neighborhood structure information, the tensor analysis has become a popular technique for hyperspectral image processing. Motivated by the above-mentioned analysis, a novel tensor-based low-rank graph with multimanifold regularization (T-LGMR) for dimensionality reduction of hyperspectral images is proposed in this paper. In the T-LGMR, a low-rank constraint is employed to preserve the global data structure while multimanifold information is utilized to enhance the discriminant ability, and tensor representation is used to preserve the spatial neighborhood information. Finally, dimensionality reduction is achieved in the graph embedding framework. Experimental results on three real hyperspectral data sets demonstrate the superiority of the proposed method over several state-of-the-art approaches.