Dual-Triple Attention Network for Hyperspectral Image Classification Using Limited Training Samples

Abstract

Hyperspectral image classification methods based on deep learning and attention mechanism have been extensively studied in recent years because of their superior performance. However, the currently applied spatial attention mechanism and channel attention mechanism are separated from each other. For this reason, we propose a new dual-triple attention network (DTAN), which realizes the high-precision classification of hyperspectral images based on capturing cross-dimensional interactive information. Specifically, DTAN is divided into two branches to extract the spectral information and spatial information of the hyperspectral image, which are called the spectral branch and spatial branch. While applying the channel attention mechanism to the spectral unit, the cross-dimensional interaction between the channel dimension and the spatial dimension is constructed. When the spatial attention mechanism is applied to the spatial branch, the correlation with the channel domain is also considered. Moreover, we introduce an efficient channel attention (ECA) module into the DenseNet, which allows the DenseNet to achieve partial cross-channel interaction. A series of experiments proved that DTAN has a significant advantage compared to other models when the training samples are minimal.