  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.
