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.

翻译：高光谱遥感（HIS）能够详细捕捉地表光谱信息，凭借其卓越的光谱诊断能力，有助于实现地表作物的精确分类与识别。然而，当前卷积神经网络（CNN）主要关注高光谱数据的局部特征，在对复杂作物类型进行分类及处理不平衡样本分布时，性能表现欠佳。相比之下，Transformer框架擅长从高光谱图像中提取全局特征。为结合两种方法的优势，本研究提出了卷积与Transformer融合网络（CMTNet）。该创新模型包含一个用于浅层特征提取的光谱-空间特征提取模块、一个结合CNN与Transformer分支以分别提取局部与全局特征的双分支结构，以及一个多输出约束模块。该模块通过多输出损失计算，并对局部、全局及联合特征施加交叉约束，从而提升分类精度。在三个数据集（WHU-Hi-LongKou、WHU-Hi-HanChuan和WHU-Hi-HongHu）上进行的大量实验表明，CMTNet在分类性能上显著优于其他先进网络，验证了其在高光谱作物分类中的有效性。