CFN: a cross-fusion network for hyperspectral image classification

Recently, deep learning networks have demonstrated strong performance in hyperspectral image (HSI) classification tasks. 2D Convolutional Neural Networks (2D-CNNs) perform well in the spatial dimension, but tend to overlook spectral information. Meanwhile, 3D Convolutional Neural Networks (3D-CNNs) perform well in the spectral dimension, but are inadequate for the exploitation of spatial and spectral information. To address this issue, a cross-fusion network (CFN) is proposed for HSI classification. Rather than simply combining 2D and 3D convolutions, CFN introduces two dedicated modules: a cross-feature module (CFM) and an adaptive cross-fusion module (ACFM). The CFM performs bidirectional cross-dimensional feature exchange between the 2D and 3D branches to capture local spatial-spectral fusion representations. The ACFM further models long-range pixel dependencies by conducting cross-branch contextual interaction and cross-residual enhancement. Based on these two modules, CFN can jointly exploit local spatial-spectral representations and long-range contextual dependencies. Experiments on the Indian Pines, Houston2013, and Pavia University datasets demonstrate that CFN achieves competitive performance compared with state-of-the-art methods.