Hyperspectral image (HSI) classification has been a hot topic for decides, as hyperspectral images have rich spatial and spectral information and provide 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 improve the accuracy of HSI classification to a new level. In this paper, NAS and Transformer are combined for handling HSI classification task for the first time. Compared with 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 network 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.

翻译：高光谱图像分类一直是研究热点，因为高光谱图像包含丰富的空间和光谱信息，为区分不同地物提供了有力依据。受益于深度学习技术的发展，基于深度学习的高光谱图像分类方法已取得显著性能。近年来，多种神经架构搜索算法被提出用于高光谱图像分类，进一步将分类精度提升至新水平。本文首次将NAS与Transformer结合处理高光谱图像分类任务。与先前工作相比，所提方法具有两大主要差异。首先，我们重新审视了先前高光谱图像分类NAS方法中设计的搜索空间，并提出一种新型混合搜索空间，该空间由空间主导单元和光谱主导单元组成。与先前工作提出的搜索空间相比，所提混合搜索空间更契合高光谱数据的特点，即高光谱图像具有较低的空间分辨率和极高的光谱分辨率。其次，为进一步提升分类精度，我们尝试将新兴的Transformer模块嫁接至自动设计的卷积神经网络上，为CNN学习的局部区域特征补充全局信息。在三个公开高光谱数据集上的实验结果表明，所提方法性能显著优于对比方法（包括人工设计网络和基于NAS的高光谱图像分类方法）。尤其在最新采集的休斯顿大学数据集上，整体精度提升了近6个百分点。代码地址：https://github.com/Cecilia-xue/HyT-NAS。