TBSSF-Net: three-branch spatial-spectral fusion network for hyperspectral image classification

Abstract

Hyperspectral images (HSI) have been extensively applied in a multitude of domains, due to their combined spatial and spectral characteristics along with a wealth of spectral bands. The ingenious combination of spatial and spectral information in HSI classification has remained a central research area for an extended period. In the classification process, it is essential to choose an expanded neighborhood window for learning. Nonetheless, employing an extensive window could lead to the problem of a lack of independence between the training dataset and the test dataset. Hence, this paper puts forward a three-branch spatial-spectral fusion network (TBSSF-Net) for HSI classification based on a smaller patch size. The network is composed of a spatial key details aggregation branch, a spatial semantic knowledge refinement branch, and a spectral band signal granularity branch. By employing the spatial branch, the network not only retains the key characteristics of details within the space but also captures the contextual relationships of global semantic information. The introduction of the spectral branch permits the combination of signal granularity at diverse levels, supplementing the performance of the spectral dimension. The TBSSF-Net has been validated for its superiority and effectiveness on four public HSI datasets. Additionally, it demonstrates significant classification performance across diverse amounts of training sets.