Transformer networks have shown impressive performance for hyperspectral interpretation. Nevertheless, the high-dimensional redundant spectral distribution of hyperspectral images (HSIs) hinders their validity of interaction between features from distant locations. In this letter, we propose the HSI-Mixer, a novel extremely simple convolution neural network (CNN), which is similar in spirit to Transformer to reconsider the remarkable inductive biases of convolutions. In specific, we construct a hybrid measurement-based linear projection (HMLP) to merge spectral signatures and spatial positions of an HSI cuboid. Meanwhile, according to the merging relations between spectral–spatial attributes, we establish both spectral and spatial Mixer blocks to separate features from a mixed volume to a pure one, across either spectral bands or spatial locations, respectively. Furthermore, our HSI-Mixer maintains the same-depth-and-resolution throughout the network. Experimental results on three benchmark datasets demonstrate that our proposal achieves promising performance, in contrast to other state-of-the-art (SOTA) methods. The codes of this work will be available at https://github.com/Blueseatear/IEEE_GRSL_2022_HSI-Mixer .

中文翻译：

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HSI-Mixer：使用来自卷积的光谱-空间混合器表示的高光谱图像分类

Transformer 网络在高光谱解释方面表现出令人印象深刻的性能。然而，高光谱图像（HSI）的高维冗余光谱分布阻碍了它们对来自遥远位置的特征之间相互作用的有效性。在这封信中，我们提出了 HSI-Mixer，这是一种新颖的极其简单的卷积神经网络 (CNN)，它在精神上类似于 Transformer，以重新考虑卷积的显着归纳偏差。具体来说，我们构建了一个基于混合测量的线性投影 (HMLP) 来合并 HSI 长方体的光谱特征和空间位置。同时，根据光谱-空间属性之间的合并关系，我们建立了光谱和空间混合器块，以跨光谱带或空间位置将混合体中的特征分离为纯体，分别。此外，我们的 HSI-Mixer 在整个网络中保持相同的深度和分辨率。三个基准数据集的实验结果表明，与其他最先进的 (SOTA) 方法相比，我们的提议取得了可喜的性能。这项工作的代码将在https://github.com/Blueseatear/IEEE_GRSL_2022_HSI-Mixer .