Hyperspectral remote sensing (HIS) enables the detailed capture of spectral information from the Earth's surface, facilitating precise classification and identification of surface crops due to its superior spectral diagnostic capabilities. However, current convolutional neural networks (CNNs) focus on local features in hyperspectral data, leading to suboptimal performance when classifying intricate crop types and addressing imbalanced sample distributions. In contrast, the Transformer framework excels at extracting global features from hyperspectral imagery. To leverage the strengths of both approaches, this research introduces the Convolutional Meet Transformer Network (CMTNet). This innovative model includes a spectral-spatial feature extraction module for shallow feature capture, a dual-branch structure combining CNN and Transformer branches for local and global feature extraction, and a multi-output constraint module that enhances classification accuracy through multi-output loss calculations and cross constraints across local, international, and joint features. Extensive experiments conducted on three datasets (WHU-Hi-LongKou, WHU-Hi-HanChuan, and WHU-Hi-HongHu) demonstrate that CTDBNet significantly outperforms other state-of-the-art networks in classification performance, validating its effectiveness in hyperspectral crop classification.

该研究介绍了一种新型模型Convolutional Meet Transformer Network (CMTNet)，它结合了卷积神经网络和Transformer，在高光谱作物分类中表现出优越性能，通过光谱和空间特征提取模块、双分支结构、多输出约束模块等实现了全局和局部特征提取以及多输出损失计算与交叉约束，显著优于其他现有网络。

CMTNet: 运用卷积与变换器网络进行高光谱图像分类