Weighted residual self-attention graph-based transformer for spectral–spatial hyperspectral image classification

ABSTRACT

Recently, deep learning for hyperspectral image classification has been successfully applied, and some convolutional neural network (CNN)-based models already achieved attractive classification results. Since hyperspectral data is a spectral-spatial cube data that can generally be considered as sequential data along with the spectral dimension, CNN models perform poorly on such a sequential data. Unlike convolutional neural networks (CNNs) that mainly concern with local relationship models in images, transformer has been shown to be a powerful structure for qualifying sequential data. In the SA (self-attention) module of ViT, each token is updated through aggregating all token’s features based on the self-attention graph. Through this, tokens can exchange information sufficiently among each other which provides a powerful representation capability. However, as the layers become deeper, the transformer model suffers from network degradation. Therefore, in order to improve the layer-to-layer information exchange and alleviate the network degradation problem, we propose a Weighted Residual Self-attention Graph-based Transformer (RSAGformer) model for hyperspectral image classification with respect to the self-attention mechanism. It effectively solves the network degradation problem of deep transformer model by fusing the self-attention information between adjacent layers and extracts the information of data effectively. Extensive experiment evaluation with six public hyperspectral datasets shows that the RSAGformer yields competitive results for classification.

KEYWORDS:

• Hyperspectral image classification
• transformer
• Weighted Residual Self-attention Graph-based Transformer
• deep learning

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the Beijing Natural Science Foundation (4214062); and by the Beijing Municipal Science and Technology Project (KM202210005023, KM202110005026); and the National Natural Science Foundation of China (61902282, 62006009)