Luo Fulin, Du Bo, Zhang Liangpei, Zhang Lefei, Tao Dacheng
IEEE Trans Cybern. 2019 Jul;49(7):2406-2419. doi: 10.1109/TCYB.2018.2810806. Epub 2018 Apr 27.
Hyperspectral image (HSI) contains a large number of spatial-spectral information, which will make the traditional classification methods face an enormous challenge to discriminate the types of land-cover. Feature learning is very effective to improve the classification performances. However, the current feature learning approaches are mostly based on a simple intrinsic structure. To represent the complex intrinsic spatial-spectral of HSI, a novel feature learning algorithm, termed spatial-spectral hypergraph discriminant analysis (SSHGDA), has been proposed on the basis of spatial-spectral information, discriminant information, and hypergraph learning. SSHGDA constructs a reconstruction between-class scatter matrix, a weighted within-class scatter matrix, an intraclass spatial-spectral hypergraph, and an interclass spatial-spectral hypergraph to represent the intrinsic properties of HSI. Then, in low-dimensional space, a feature learning model is designed to compact the intraclass information and separate the interclass information. With this model, an optimal projection matrix can be obtained to extract the spatial-spectral features of HSI. SSHGDA can effectively reveal the complex spatial-spectral structures of HSI and enhance the discriminating power of features for land-cover classification. Experimental results on the Indian Pines and PaviaU HSI data sets show that SSHGDA can achieve better classification accuracies in comparison with some state-of-the-art methods.
高光谱图像（HSI）包含大量的空间光谱信息，这将使传统分类方法在区分土地覆盖类型上面临巨大挑战。特征学习对于提高分类性能非常有效。然而，当前的特征学习方法大多基于简单的内在结构。为了表征HSI复杂的内在空间光谱，基于空间光谱信息、判别信息和超图学习，提出了一种新颖的特征学习算法，称为空间光谱超图判别分析（SSHGDA）。SSHGDA构建了一个重建类间散度矩阵、一个加权类内散度矩阵、一个类内空间光谱超图和一个类间空间光谱超图，以表征HSI的内在特性。然后，在低维空间中，设计一个特征学习模型来压缩类内信息并分离类间信息。利用该模型，可以获得一个最优投影矩阵来提取HSI的空间光谱特征。SSHGDA可以有效地揭示HSI复杂的空间光谱结构，并增强土地覆盖分类特征的判别能力。在印度松树和帕维亚大学HSI数据集上的实验结果表明，与一些现有方法相比，SSHGDA可以实现更好的分类精度。
