数字农科院2.0

MBSFC: hyperspectral image classification based on multi-branch and spectral feature conversion

文献类型： 外文期刊

作者： Tang, Ting;Liu, Shaopeng;Fu, Xueliang;Yan, Weihong;Luo, Xiaoling;Pan, Xin

作者机构：

关键词： Hyperspectral image classification;deep learning;attention mechanism;convolutional neural network;multi-branch architecture;small training set

期刊名称： INTERNATIONAL JOURNAL OF REMOTE SENSING

ISSN： 0143-1161

年卷期： 2024 年

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： Hyperspectral image classification (HSI) is a vital aspect of remote sensing technology. However, the high-dimensional nature of HSI and the continuous spectral bands significantly impact its classification performance. In this paper, we propose a classification method called MBSFC for HSI classification. MBSFC utilizes multi-branch and spectral feature conversion (SFC) techniques to eliminate redundant information and extract valuable features. Firstly, to preserve spectral-spatial information, the SFC block is introduced to eliminate redundant information through up-sampling. Subsequently, the feature maps from the SFC block and the spectral bands reduced by a factor of four are fed into three branches: the spectral branch, spatial-X branch, and spatial-Y branch. Each branch employs a 3D-CNN-based dense block and attention mechanism to extract useful features. Finally, the obtained features from the three branches are fused for HSI classification. To cater to different application scenarios, we divide MBSFC into three models with different network structures: MBSFC-s, MBSFC-m, and MBSFC-l. The MBSFC-l model, with a three-branch structure, achieves the best performance. The three models differ in the number of branches in the feature extraction. Experimental results on four publicly available hyperspectral datasets show that MBSFC achieves competitive results with small samples compared to other state-of-the-art methods. The code is available at https://github.com/TeresaTing/MBSFC.

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