Dual-View Spectral and Global Spatial Feature Fusion Network for Hyperspectral Image Classification

For hyperspectral image (HSI) classification, two branch networks generally use convolutional neural networks (CNNs) to extract the spatial features and long short-term memory (LSTM) to learn the spectral features. However, CNNs with a local kernel neglect the global properties of the whole HSI. LSTM does not consider the macroscopic and detailed information of spectra. In this article, we propose a dual-view spectral and global spatial feature fusion network (DSGSF) to extract the spatial-spectral features for HSI classification (HSIC), including a spatial subnetwork and a spectral subnetwork. In the spatial subnetwork, we propose a global spatial feature representation model based on the encoder-decoder structure with channel attention and spatial attention to learn the global spatial features. In the spectral subnetwork, we design a dual-view spectral feature aggregation model with view attention to learn the diversity of spectral features. By fusing the two subnetworks, we construct DSGSF to extract the spatial-spectral features of HSI with strong discriminating performance. Experimental results on three public datasets illustrate that the proposed method can achieve competitive results compared with the state-of-the-art methods. Code: https://github.com/RZWang-WH/DSGSF.

Automated summary

In this article, a dual-view spectral and global spatial feature fusion network (DSGSF) is proposed for hyperspectral image (HSI) classification. The DSGSF consists of a spatial subnetwork and a spectral subnetwork, which can extract spatial-spectral features with strong discriminating performance.