SSF-Net: A Spatial–Spectral Features Integrated Autoencoder Network for Hyperspectral Unmixing

Abstract

In recent years, deep learning has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities. Particularly, the unsupervised unmixing method based on an autoencoder (AE) has become a research hotspot. Most of the current AE unmixing networks mainly focus on information about pixels and their neighborhoods in images. However, they make insufficient use of information about spatial heterogeneity and spectral differences of endmembers in hyperspectral image (HSI) data. To this end, an AE HU network with the name of SSF-Net is proposed for fusing the spatial–spectral features. The network first extracts pseudoendmember information from the HSI using a regional vertex component analysis algorithm. Then, a dual-branch feature fusion module incorporating a spatial–spectral attention mechanism is constructed to make full use of the information in the HSI data, thereby improving the network's unmixing performance. It is worth stating that SSF-Net can fuse spatial–spectral information and utilize different attention maps to obtain more significant spectral difference information and more discriminative spatial difference information about the scene. The experimental results on synthetic and real datasets demonstrate that the proposed SSF-Net outperforms state-of-the-art unmixing algorithms.

Keywords

• Attention
• autoencoder (AE)
• deep learning (DL)
• feature fusion
• hyperspectral unmixing (HU)