计算机与数字工程2024，Vol.52Issue(3)：898-902,949,6.DOI:10.3969/j.issn.1672-9722.2024.03.044
基于三维卷积神经网络的高光谱图像分类
Hyperspectral Image Classification Based on Three-dimensional Convolutional Neural Network
张岩 1沈金悦 1邵钰奕 1卢瑶1
作者信息
- 1. 青岛科技大学机电工程学院 青岛 266061
- 折叠
摘要
Abstract
Aiming at the phenomenon that traditional hyperspectral image classification methods are difficult to effectively ex-tract space-spectral joint information and convolutional neural networks are difficult to effectively pay attention to important fea-tures,this paper proposes a hyperspectral image based on convolutional neural network 3D-CNN combined with CBAM attention mechanism classification.Hyperspectral data brings difficulties to feature extraction due to its high dimensionality.Therefore,this paper uses three-dimensional convolutional neural network(3D-CNN)to extract features,combined with visual attention mecha-nism to make convolutional neural network more important.feature.Through verification on two public data sets,it is proved that the method in this paper can achieve better classification accuracy.关键词
图像分类/卷积神经网络/注意力机制/分类精度Key words
image classification/convolutional neural network/attention mechanism/classification accuracy分类
信息技术与安全科学引用本文复制引用
张岩,沈金悦,邵钰奕,卢瑶..基于三维卷积神经网络的高光谱图像分类[J].计算机与数字工程,2024,52(3):898-902,949,6.
