Hyperspectral image (HSI) classification has been a hot topic for decides, as HSIs have rich spatial and spectral information, and provide a strong basis for distinguishing different land-cover objects. Benefiting from the development of deep learning technologies, deep learning-based HSI classification methods have achieved promising performance. Recently, several neural architecture search (NAS) algorithms have been proposed for HSI classification, which further improves the accuracy of HSI classification to a new level. In this article, NAS and transformer are combined for handling the HSI classification task for the first time. Compared with the previous work, the proposed method has two main differences. First, we revisit the search spaces designed in previous HSI classification NAS methods and propose a novel hybrid search space, consisting of the space-dominated cell and the spectrum-dominated cell. Compared with search spaces proposed in previous works, the proposed hybrid search space is more aligned with the characteristic of HSI data, that is, HSIs have a relatively low spatial resolution and an extremely high spectral resolution. Second, to further improve the classification accuracy, we attempt to graft the emerging transformer module on the automatically designed convolutional neural network (CNN) to add global information to local region focused features learned by CNN. Experimental results on three public HSI datasets show that the proposed method achieves much better performance than comparison approaches, including manually designed networks and NAS-based HSI classification methods. Especially on the most recently captured dataset Houston University, overall accuracy is improved by nearly 6 percentage points. Code is available at https://github.com/Cecilia-xue/HyT-NAS.

中文翻译：

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自动设计的卷积神经网络上的嫁接变压器用于高光谱图像分类


高光谱图像（HSI）分类一直是决策的热点，因为HSI具有丰富的空间和光谱信息，为区分不同的土地覆盖对象提供了强有力的基础。受益于深度学习技术的发展，基于深度学习的HSI分类方法取得了可喜的性能。最近，针对HSI分类提出了几种神经架构搜索（NAS）算法，这进一步将HSI分类的准确性提高到了一个新的水平。在本文中，首次将 NAS 和 Transformer 结合起来处理 HSI 分类任务。与之前的工作相比，所提出的方法有两个主要区别。首先，我们重新审视之前 HSI 分类 NAS 方法中设计的搜索空间，并提出一种新颖的混合搜索空间，由空间主导单元和频谱主导单元组成。与之前的工作中提出的搜索空间相比，所提出的混合搜索空间更符合HSI数据的特征，即HSI具有相对较低的空间分辨率和极高的光谱分辨率。其次，为了进一步提高分类精度，我们尝试将新兴的 Transformer 模块移植到自动设计的卷积神经网络（CNN）上，以将全局信息添加到 CNN 学习的局部区域聚焦特征中。在三个公共 HSI 数据集上的实验结果表明，所提出的方法比手动设计的网络和基于 NAS 的 HSI 分类方法等比较方法取得了更好的性能。尤其是在最近捕获的休斯顿大学数据集上，整体准确率提高了近 6 个百分点。代码可在 https://github 上获取。com/Cecilia-xue/HyT-NAS。