Two-Stage Attention Network for hyperspectral image classification

Abstract

ABSTRACT Considering that the hyperspectral image (HSI) has a large number of spectrum bands, to optimize the features and make full use of more informative features, many papers have introduced attention mechanism to the models based on three-dimensional (3D) convolution. However, though the number of spectrum bands is large, there are many useless bands and noise, which may generate lots of useless features into the subsequent network and affect the learning efficiency of each convolutional layer. Therefore, how to reduce the influence of noise from HSI data itself and the classification process is key to the HSI classification tasks. In this paper, we proposed a 3D convolutional neural network (3D-CNN) based two-stage attention network (TSAN) for HSI classification. For one thing, the spectral-wise attention module in the first stage can optimize the whole spectrum by shielding useless spectrum bands and reducing the noise in the spectrum. For another, more discriminative spectral–spatial features are extracted and sent to the subsequent layers by channel-wise attention mechanism combined with soft thresholding in the second stage. In addition, we introduced non-local block to learn global spatial features and used a multi-scale network to combine the local space and the global space. The experiments carried out on three HSI datasets show that our proposed network for HSI classification tasks can indeed reduce the noise by soft thresholding and achieve promising classification performance.