Fusion of local and important channel information for multi-attention hyperspectral image classification

Zijie Huo; Zhen Yang; Xin Zhou; Zhijian Yin; Tao Zhang

doi:10.1117/12.3033533

13 June 2024 Fusion of local and important channel information for multi-attention hyperspectral image classification

Zijie Huo, Zhen Yang, Xin Zhou, Zhijian Yin, Tao Zhang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131800S (2024) https://doi.org/10.1117/12.3033533
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

In recent years, methods based on deep convolutional neural networks (CNNs) have gradually become the focus of research in the field of hyperspectral image (HSI) classification. It is well known that hyperspectral data itself contains spatial and spectral information. While CNN-based methods have advantages in extracting local spatial features, they are not good at handling spectral features and global information. Therefore, this paper proposes a multi-attention network that fuses local and key channel information to complete the task of HSI classification. First, the principal component analysis (PCA) is used to pre-process the HSI data. Second, a feature information fusion module based on the SE module and 2D convolution is constructed to fuse local spatial information and enhanced feature channel information. Third, the global covariance pooling function accelerates the convergence rate of the network. Finally, the fused features are sent to the Vision Transformer (ViT) module for position encoding to capture global sequential information and improve the hyperspectral image classification results. Experiments carried out on several typical three public datasets demonstrate that the proposed network method can provide competitive results compared to the other state-of-the-art HSI networks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zijie Huo, Zhen Yang, Xin Zhou, Zhijian Yin, and Tao Zhang "Fusion of local and important channel information for multi-attention hyperspectral image classification", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131800S (13 June 2024); https://doi.org/10.1117/12.3033533

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KEYWORDS

Image classification

Hyperspectral imaging

Data modeling

Feature extraction

Convolution

Image fusion

Information fusion

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