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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Dimensionality Reduction of Hyperspectral Imagery Using Sparse Graph Learning
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Dimensionality Reduction of Hyperspectral Imagery Using Sparse Graph Learning

机译：基于稀疏图学习的高光谱图像降维

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摘要

Combining with sparse representation, the sparse graph can adaptively capture the intrinsic structural information of the specified data. In this paper, an unsupervised sparse-graph-learning-based dimensionality reduction (SGL-DR) method is proposed for hyperspectral image. In SGL-DR, the sparse graph construction and projection learning are combined together in a unified framework and influence each other. During sparse graph learning, projected features are utilized to enhance the discriminant information in sparse graph. Likewise, in projection learning, the enhanced sparse graph could make projected features have high discriminant capacity. Besides, the spatial-spectral information in the original space combined with the structure information in the projected space is also exploited to learn the imprecise discriminant information. With the imprecise discriminant information, the projected space that is spanned by the projection matrix of the constructed sparse graph would contain abundant discriminant information, which is beneficial for hyperspectral image classification. Experimental results over two hyperspectral image datasets demonstrate that the proposed approach outperforms the other state-of-the-art unsupervised approaches with a 10% improvement of the classification accuracy. Furthermore, it also outperforms those graph-based supervised methods with acceptable computational cost.
机译：结合稀疏表示，稀疏图可以自适应地捕获指定数据的固有结构信息。本文提出了一种基于无监督稀疏图学习的降维方法（SGL-DR）。在SGL-DR中，稀疏图的构造和投影学习在一个统一的框架中结合在一起，并且相互影响。在稀疏图学习期间，将使用投影特征来增强稀疏图中的判别信息。同样，在投影学习中，增强的稀疏图可以使投影特征具有较高的判别能力。此外，还利用原始空间中的空间光谱信息与投影空间中的结构信息相结合来学习不精确的判别信息。利用不精确的判别信息，所构造的稀疏图的投影矩阵所跨越的投影空间将包含丰富的判别信息，这对于高光谱图像分类是有利的。在两个高光谱图像数据集上的实验结果表明，该方法优于其他最新的无监督方法，分类精度提高了10％。此外，它在可接受的计算成本上也优于那些基于图的监督方法。

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  • 作者

    Puhua Chen; Licheng Jiao; Fang Liu; Shuiping Gou; Jiaqi Zhao; Zhiqiang Zhao;

  • 作者单位

    Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi'an, China;

    Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi'an, China;

    School of Computer Science and Technology and the Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, China;

    Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi'an, China;

    Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi'an, China;

    Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi'an, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Hyperspectral imaging; Sparse matrices; Computational efficiency; Dictionaries; Symmetric matrices;

    机译：高光谱成像;稀疏矩阵;计算效率;字典;对称矩阵;

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