Hyper-LGNet: Coupling Local and Global Features for Hyperspectral Image Classification

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-10, Vol.14 (20), p.5251
• Main Authors: Zhang, Tianxiang, Wang, Wenxuan, Wang, Jing, Cai, Yuanxiu, Yang, Zhifang, Li, Jiangyun
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; CNN; Coupling; Datasets; Deep learning; dual-flow architecture; Feature extraction; hyperspectral image; Hyperspectral imaging; Image analysis; Image classification; Image processing; Information processing; Land cover; Machine learning; Neural networks; Object recognition; Remote sensing; Spatial data; Target detection; Transformer; Transformers
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14205251

Hyperspectral sensors provide an opportunity to capture the intensity of high spatial/spectral information and enable applications for high-level earth observation missions, such as accurate land cover mapping and target/object detection. Currently, convolutional neural networks (CNNs) are good at coping with hyperspectral image processing tasks because of the strong spatial and spectral feature extraction ability brought by hierarchical structures, but the convolution operation in CNNs is limited to local feature extraction in both dimensions. In the meanwhile, the introduction of the Transformer structure has provided an opportunity to capture long-distance dependencies between tokens from a global perspective; however, Transformer-based methods have a restricted ability to extract local information because they have no inductive bias, as CNNs do. To make full use of these two methods’ advantages in hyperspectral image processing, a dual-flow architecture named Hyper-LGNet to couple local and global features is firstly proposed by integrating CNN and Transformer branches to deal with HSI spatial-spectral information. In particular, a spatial-spectral feature fusion module (SSFFM) is designed to maximally integrate spectral and spatial information. Three mainstream hyperspectral datasets (Indian Pines, Pavia University and Houston 2013) are utilized to evaluate the proposed method’s performance. Comparative results show that the proposed Hyper-LGNet achieves state-of-the-art performance in comparison with the other nine approaches concerning overall accuracy (OA), average accuracy (AA) and kappa index. Consequently, it is anticipated that, by coupling CNN and Transformer structures, this study can provide novel insights into hyperspectral image analysis.