Hyperspectral image classification based on hybrid convolution with three-dimensional attention mechanism

被引：1
|
作者：
Zhao X. [1 ,2 ]
Niu J. [1 ,2 ]
Liu C. [1 ,2 ]
Xia Y. [1 ,2 ]
机构：
[1] Missile Engineering College, Rocket Force Engineering University, Xi'an
[2] Armament Launch Theory and Technology Key Discipline Laboratory of China, Xi'an
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 09期
关键词：
hybrid convolution; hyperspectral image classification; spatial-spectral feature extraction; three-dimensional attention mechanism;
D O I：
10.12305/j.issn.1001-506X.2023.09.04
中图分类号：
学科分类号：
摘要：
Aiming at the lack of effective attention in the process of feature extraction in existing hyperspectral image classification models, a classification model based on hybrid convolution and three-dimensional attention mechanism is proposed. The method realizes the extraction of spatial-spectral features of hyperspectral images by tandem three-dimensioal (3D) convolution and two-dimensional (2D) convolution. An attention mechanism in the 3D convolution stage is designed, and the attention mechanism is implemented in the 3D convolution stage to realize the attention and activation of the effective spatial-spectral features of hyperspectral images while the model is extracting the underlying features. Compared with the traditional 3D convolution-based model, the classification model proposed in this paper reduces the complexity of operations, improves the model's ability to suppress interference noise, and enhances the classification effect. Ablation experiments against the method demonstrate the effectiveness of the proposed 3D convolution attention mechanism, and the optimal classification accuracy is achieved in comparison experiments with five other classification models on two publicly available datasets, Indian Pines and Pavia University. © 2023 Chinese Institute of Electronics. All rights reserved.
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收藏
页码：2673 / 2680
页数：7
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