Hyperspectral image (HSI) provided with a substantial number of correlated bands causes calculation consumption and an undesirable “dimension disaster” problem for the classification. Band selection (BS) is an effective measure to reduce the information redundancy with the physics spectrum preserved for HSI. Although the existing BS methods have achieved noticeable progress, the correlation between neighbor bands still needs to be mined deeply for an effective selection criterion. This article proposes a BS approach to collecting the discriminative band subset for HSI classification (HSIC), which adopts the self-supervised learning paradigm to implement the BS by the auxiliary spectrum rebuilding (SR) task. In specific, we utilized a convolutional neural network (CNN) and a graph convolutional network (GCN) for the spectral–spatial feature extraction. Next, GCN and CNN are developed for the refinement of the band correlation sequentially. Afterward, the selected bands in terms of the acquired correlation are fed into the presented self-supervised SR network for spectral reconstruction. Simultaneously, the proposed architecture completed the selection with the optimization of the band reconstruction by a defined loss function. In this way, we supply substitution for selection criterion and path searching through the end-to-end framework. The extensive experimental results and analysis demonstrated that the proposed hybrid architecture provided a competitive band subset for the classification, and the accuracies with different types of classifiers are more effective than the compared BS methods.

中文翻译：

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通过混合图卷积网络进行无监督高光谱波段选择

提供大量相关波段的高光谱图像 (HSI) 会导致计算消耗和分类的不良“维度灾难”问题。波段选择 (BS) 是减少信息冗余的有效措施，同时保留了 HSI 的物理谱。尽管现有的 BS 方法取得了显着进展，但仍需要深入挖掘相邻频段之间的相关性，以获得有效的选择标准。本文提出了一种 BS 方法来收集用于 HSI 分类 (HSIC) 的判别频带子集，该方法采用自监督学习范式通过辅助频谱重建 (SR) 任务来实现 BS。具体来说，我们利用卷积神经网络 (CNN) 和图卷积网络 (GCN) 进行光谱空间特征提取。接下来，开发 GCN 和 CNN 以依次细化波段相关性。之后，根据获得的相关性选择的频段被馈送到所提出的自监督 SR 网络中进行频谱重建。同时，所提出的架构通过定义的损失函数优化频带重建完成了选择。通过这种方式，我们通过端到端框架为选择标准和路径搜索提供替代。广泛的实验结果和分析表明，所提出的混合架构为分类提供了一个有竞争力的频段子集，