Deep learning has made significant progress in hyperspectral image (HSI) classification, and its powerful ability to automatically learn abstract features is well recognized. Recently, the simple architecture of multi-layer perceptron (MLP) has been extensively employed to extract long-range dependencies of HSI and achieved impressive results. However, existing MLP-based models exhibit insufficient representation of spectral–spatial information in HSI and generally aggregate features with fixed weights, which limits their ability to capture semantic differences. To tackle these challenges, this paper proposes a novel spectral–spatial wave network (SWaveNet) for HSI classification tasks to enhance the representation capability of spectral–spatial features in ground objects. Specifically, the spectral–spatial wave mixer (SWaveMixer) block is designed as a key component to represent each HSI input as a wave function with amplitude and phase parts. Thus, it enables a deeper dynamic perception and facilitates the extraction of spectral–spatial feature variations of ground objects. The amplitude represents the original features and the phase term is a complex value changing based on the semantic contents of the input images. Furthermore, the inception unit is introduced into the SWaveMixer block to consider spectral–spatial information at multiple granularity levels. Experiments conducted on five public datasets demonstrate the superiority of SWaveNet in classification performance and generalization compared to competitors.

中文翻译：

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S[公式省略]WaveNet：一种用于高光谱图像分类的新型谱空间波网络

深度学习在高光谱图像（HSI）分类方面取得了重大进展，其强大的自动学习抽象特征的能力得到了广泛认可。最近，多层感知器（MLP）的简单架构已被广泛应用于提取 HSI 的长程依赖性，并取得了令人印象深刻的结果。然而，现有的基于 MLP 的模型在 HSI 中对光谱空间信息的表示不足，并且通常以固定权重聚合特征，这限制了它们捕获语义差异的能力。为了应对这些挑战，本文提出了一种用于 HSI 分类任务的新型光谱空间波网络（SWaveNet），以增强地物光谱空间特征的表示能力。具体来说，频谱空间波混频器 (SWaveMixer) 块被设计为关键组件，用于将每个 HSI 输入表示为具有幅度和相位部分的波函数。因此，它能够实现更深入的动态感知，并有助于提取地物的光谱空间特征变化。幅度表示原始特征，相位项是根据输入图像的语义内容变化的复数值。此外，SWaveMixer 模块中引入了初始单元，以考虑多个粒度级别的频谱空间信息。在五个公共数据集上进行的实验证明了 SWaveNet 与竞争对手相比在分类性能和泛化方面的优越性。