The representation-based algorithm has raised a great interest in hyperspectral image (HSI) classification. $l_1$-minimization-based sparse representation (SR) attempts to select a few atoms and cannot fully reflect within-class information, while $l_2$-minimization-based collaborative representation (CR) tries to use all of the atoms leading to mixed-class information. Considering the above problems, we propose the pairwise elastic net representation-based classification (PENRC) method. PENRC combines the $l_1$-norm and $l_2$-norm penalties and introduces a new penalty term, including a similar matrix between dictionary atoms. This similar matrix enables the automatic grouping selection of highly correlated data to estimate more robust weight coefficients for better classification performance. To reduce computation cost and further improve classification accuracy, we use part of the atoms as a local adaptive dictionary rather than the entire training atoms. Furthermore, we consider the neighbor information of each pixel and propose a joint pairwise elastic net representation-based classification (J-PENRC) method. Experimental results on chosen hyperspectral data sets confirm that our proposed algorithms outperform the other state-of-the-art algorithms.

中文翻译：

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用于高光谱图像分类的基于成对弹性网络表示的分类

基于表示的算法引起了人们对高光谱图像（HSI）分类的极大兴趣。 ${升}_1$-基于最小化的稀疏表示（SR）试图选择几个原子并且不能完全反映类内信息，而 ${升}_2$-基于最小化的协作表示 (CR) 尝试使用导致混合类信息的所有原子。考虑到上述问题，我们提出了基于成对弹性网络表示的分类（PENRC）方法。PENRC 结合了${升}_1$-规范和 ${升}_2$-norm 惩罚并引入了一个新的惩罚项，包括字典原子之间的相似矩阵。这种相似的矩阵使高度相关数据的自动分组选择能够估计更稳健的权重系数以获得更好的分类性能。为了降低计算成本并进一步提高分类精度，我们使用部分原子作为局部自适应字典而不是整个训练原子。此外，我们考虑了每个像素的邻居信息，并提出了一种基于联合成对弹性网络表示的分类（J-PENRC）方法。所选高光谱数据集的实验结果证实，我们提出的算法优于其他最先进的算法。