In recent years, deep neural networks have exhibited numerous advantages in hyperspectral image classification (HIC). However, owing to the limited number of training samples of hyperspectral images (HSIs), the network structure should not be designed too deep to retard the overfitting phenomenon. This study proposes a cascaded dual-scale crossover network for HIC, which not only could extract rich features, but also does not make the network deeper. It continuously connects two different cascaded dual-scale crossover blocks, and automatically extracts the spectral–spatial features of HSIs. Moreover, for the limited training samples, the proposed network could flexibly capture more discriminant contextual features by using different spectral-size and spatial-size convolution kernels. Furthermore, two different cross-merge methods are designed to improve the information flow and contrast of the images to obtain parts of interest for the images. Two skip structures are also used for alleviating overfitting and accelerating the network training. Additional experimental results on some datasets, including Indian Pines, Kennedy Space Center, and University of Pavia, verify the feasibility of the proposed network. Namely, the classification accuracy of the proposed network is superior to that of other existing networks.

近年来，深度神经网络在高光谱图像分类（HIC）中表现出许多优势。但是，由于高光谱图像（HSI）训练样本的数量有限，因此网络结构不应设计得太深以阻止过度拟合现象。这项研究提出了一种用于HIC的级联双尺度交叉网络，该网络不仅可以提取丰富的功能，而且不会使网络更深。它连续连接两个不同的级联双标度交叉模块，并自动提取HSI的光谱空间特征。而且，对于有限的训练样本，所提出的网络可以通过使用不同的频谱大小和空间大小的卷积核来灵活地捕获更多可区分的上下文特征。此外，设计了两种不同的交叉合并方法，以改善图像的信息流和对比度，以获得图像感兴趣的部分。还使用两个跳过结构来减轻过拟合并加速网络训练。在一些数据集上的其他实验结果，包括印度松树，肯尼迪航天中心和帕维亚大学，证明了该网络的可行性。即，提出的网络的分类精度优于其他现有网络的分类精度。验证拟议网络的可行性。即，提出的网络的分类精度优于其他现有网络的分类精度。验证拟议网络的可行性。即，所提出的网络的分类精度优于其他现有网络的分类精度。