由于不同高光谱数据集在频带数、光谱范围和空间分辨率上存在显著差异，适用于不同高光谱数据集的最优网络结构也存在不同。此外，人工设计的深度学习网络需要调整大量的超参数，这无疑给设计一个适用于各种HSI数据集的通用分类模型带来了严重的挑战。本文提出了一种结合高效注意力机制的神经架构搜索算法，实现深度学习网络的自动设计以避免人工设计网络的偏差。首先，为了构建高效的搜索过程，本文构建了基于可微网络架构搜索的模型，该方法可以有效地提高超参数网络的搜索速度。然后，为了实现高精度的分类结果，本文设计了一个新型的模块化搜索空间。最后，考虑到高光谱数据集类不平衡带来的误分类问题，本文采用Poly损失函数增加少数类别的损失权重，从而提高模型对这些类别的识别能力。在公开高光谱数据集上的实验结果表明，本文方法的总体分类精度分别达到了99.50%、97.81%。本文提出的方法探索了神经架构搜索在高光谱分类任务上的应用，提高了分类精度和算法设计的效率。AI语音播报 

Due to the significant differences in the number of bands, spectral range and spatial resolution of different hyperspectral image datasets, the optimal network structures applicable to different hyperspectral image datasets also differ. In addition, manually designed deep learning networks need to tune a large number of hyperparameters, which undoubtedly poses a serious challenge to designing a generalized classification model applicable to various HSI datasets. Therefore, an efficient attention neural architecture search algorithm is proposed to realize the automatic design of deep learning networks. Firstly, in order to construct an efficient search process, a model is constructed based on the search of microable network architecture, which can effectively improve the search speed of hyperparametric networks. Then, in order to achieve high-precision classification results, a novel modular search space is designed. Finally, considering the misclassification problem of small samples in hyperspectral datasets, Poly loss function is used to increase the loss weights of a few categories, so as to improve the model’s ability to recognize these categories. Experimental results on publicly available hyperspectral datasets show that the overall classification accuracy of the proposed method reaches 99.50% and 97.81%, respectively. The proposed method explores the application of neural architecture search in hyperspectral classification tasks, improving classification accuracy and algorithm design efficiency.