Hyperspectral image classification based on dual-branch attention network with 3-D octave convolution

Abstract

Hyperspectral Image (HSI) classification aims to assign each hyperspectral pixel with an appropriate land-cover category. In recent years, deep learning (DL) has received attention from a growing number of researchers. Hyperspectral image classification methods based on DL have shown admirable performance, but there is still room for improvement in terms of exploratory capabilities in spatial and spectral dimensions. To improve classification accuracy and reduce training samples, we propose a double branch attention network (OCDAN) based on 3-D octave convolution and dense block. Especially, we first use a 3-D octave convolution model and dense block to extract spatial features and spectral features respectively. Furthermore, a spatial attention module and a spectral attention module are implemented to highlight more discriminative information. Then the extracted features are fused for classification. Compared with the state-of-the-art methods, the proposed framework can achieve superior performance on two hyperspectral datasets, especially when the training samples are signally lacking. In addition, ablation experiments are utilized to validate the role of each part of the network.