In this paper, NAS and Transformer are combined for handling HSI classification task for the first time. Compared with previous works, the proposed method has two main differences. Firstly, we revisit 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. Secondly, for further improving 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. [Paper] [code]
