Combined techniques of sparse representation (SR) and low-rank representation (LRR) are commonly used for hyperspectral image (HSI) classification. Although they have the ability to capture the interclass representations of data for HSI classification, they ignore the adaptive key connectivity of the learned intraclass data representations in particular with the high-dimensional complex HSI data. It is well-known that the key connectivity of graph-based algorithms is crucial for subspace learning because of the guarantees of its good neighbors. For this purpose, a novel sparse and low-rank representation with key connectivity (SLRC) method is proposed for HSI classification. To be specific, the adaptive probability graph structure is developed to integrate the SR and LRR regularizations to formulate the SLRC model, which flexibly perform discriminative latent subspace construction and preserve the key connectivity of intraclass representations. Then, extensive experiments are executed based on three popular HSI datasets, which demonstrates that the SLRC method outperforms the other popular methods.

中文翻译：

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用于高光谱图像分类的具有关键连通性的稀疏和低秩表示

稀疏表示 (SR) 和低秩表示 (LRR) 的组合技术通常用于高光谱图像 (HSI) 分类。尽管它们有能力为 HSI 分类捕获数据的类间表示，但它们忽略了学习到的类内数据表示的自适应关键连接，特别是与高维复杂 HSI 数据的关联。众所周知，基于图的算法的关键连通性对于子空间学习至关重要，因为它具有良好邻居的保证。为此，针对 HSI 分类提出了一种新颖的具有关键连接性的稀疏和低秩表示（SLRC）方法。具体而言，开发了自适应概率图结构以整合 SR 和 LRR 正则化以制定 SLRC 模型，它灵活地执行有区别的潜在子空间构造并保留类内表示的关键连接。然后，基于三个流行的 HSI 数据集执行了大量实验，这表明 SLRC 方法优于其他流行方法。