Dimensionality Reduction of Hyperspectral Imagery Using Sparse Graph Learning
Dimensionality Reduction of Hyperspectral Imagery Using Sparse Graph Learning
复制标题
使用稀疏图学习对高光谱图像进行降维
DOI：
10.1109/jstars.2016.2606578
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发表时间：
2017
影响因子：
5.5
通讯作者：
Chen PH
中科院分区：
文献类型：
--
作者：
Chen Puhua;Jiao Licheng;Gou Shuiping;Zhao Jiaqi;Zhao Zhiqiang;Liu Fang;Liu Fang;Chen PH
结合稀疏表示，稀疏图可以自适应地捕捉指定数据的内在结构信息。提出了一种基于稀疏图学习的无监督降维方法。在SGL-DR中，稀疏图构造和投影学习结合在一个统一的框架中，并且相互影响。在稀疏图学习过程中，利用投影特征增强稀疏图的鉴别信息。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鉴别能力。此外，在原始空间的空间光谱信息结合在投影空间的结构信息也被用来学习不精确的判别信息。在不精确判别信息的情况下，构造稀疏图的投影矩阵所覆盖的投影空间将包含丰富的判别信息，有利于高光谱图像分类。在两个高光谱图像数据集上的实验结果表明，该方法优于其他国家的最先进的无监督方法的分类精度提高了10%。此外，它还优于那些基于图的监督方法，且计算成本可接受。
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.