Spatial_spectral classification (SSC) has become a trend for hyperspectral image (HSI) classification. However, most SSC methods mainly consider local information, so that some correlations may not be effectively discovered when they appear in regions that are not contiguous. Although many SSC methods can acquire spatial-contextual characteristics via spatial filtering, they lack the ability to consider correlations in non-Euclidean spaces. To address the aforementioned issues, we develop a new semisupervised HSI classification approach based on normalized spectral clustering with kernel-based learning (NSCKL), which can aggregate local-to-global correlations to achieve a distinguishable embedding to improve HSI classification performance. In this work, we propose a normalized spectral clustering (NSC) scheme that can learn new features under a manifold assumption. Specifically, we first design a kernel-based iterative filter (KIF) to establish vertices of the undirected graph, aiming to assign initial connections to the nodes associated with pixels. The NSC first gathers local correlations in the Euclidean space and then captures global correlations in the manifold. Even though homogeneous pixels are distributed in noncontiguous regions, our NSC can still aggregate correlations to generate new (clustered) features. Finally, the clustered features and a kernel-based extreme learning machine (KELM) are employed to achieve the semisupervised classification. The effectiveness of our NSCKL is evaluated by using several HSIs. When compared with other state-of-the-art (SOTA) classification approaches, our newly proposed NSCKL demonstrates very competitive performance. The codes will be available at https://github.com/yuanchaosu/TCYB-nsckl.

中文翻译：

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NSCKL：基于核学习的归一化光谱聚类用于半监督高光谱图像分类


空间光谱分类（SSC）已成为高光谱图像（HSI）分类的趋势。然而，大多数SSC方法主要考虑局部信息，因此当某些相关性出现在不连续的区域时，可能无法有效地发现它们。尽管许多SSC方法可以通过空间滤波获取空间上下文特征，但它们缺乏考虑非欧几里得空间中的相关性的能力。为了解决上述问题，我们开发了一种基于归一化谱聚类和基于内核的学习（NSCKL）的新半监督 HSI 分类方法，该方法可以聚合局部到全局相关性以实现可区分的嵌入，从而提高 HSI 分类性能。在这项工作中，我们提出了一种归一化谱聚类（NSC）方案，可以在流形假设下学习新特征。具体来说，我们首先设计一个基于内核的迭代过滤器（KIF）来建立无向图的顶点，旨在将初始连接分配给与像素相关的节点。 NSC 首先收集欧几里德空间中的局部相关性，然后捕获流形中的全局相关性。即使同质像素分布在不连续的区域中，我们的 NSC 仍然可以聚合相关性以生成新的（聚集的）特征。最后，采用聚类特征和基于内核的极限学习机（KELM）来实现半监督分类。我们的 NSCKL 的有效性是通过使用多个 HSI 来评估的。与其他最先进的（SOTA）分类方法相比，我们新提出的 NSCKL 表现出了非常有竞争力的性能。代码可在 https://github.com/yuanchaosu/TCYB-nsckl 获取。