液晶与显示2024，Vol.39Issue(6)：833-843,11.DOI:10.37188/CJLCD.2023-0175
基于空间金字塔注意力机制残差网络的高光谱图像分类
Hyperspectral image classification based on spatial pyramid attention mechanism combined with ResNet
摘要
Abstract
In order to extract spatial-spectral joint features of hyperspectral images,this paper proposes a hyperspectral image classification model based on an improved spatial pyramid attention mechanism residual network.Firstly,principal component analysis is used to remove spectral redundancy,and combined with spatial pyramid attention mechanism,a residual network based hyperspectral image classification model is improved to obtain refined features.Then,the spatial pyramid attention model is used to achieve multi-scale joint feature attention,improve sensitivity to joint features,and effectively emphasize and focus on spatial and spectral information for information exchange.Finally,the classification label is obtained through the Softmax classifier.The proposed method in this paper is tested on MUUFL and Trento datasets,and the experimental results show that the overall classification accuracy of the proposed algorithm reaches 94.08%and 98.32%,respectively.Compared to other hyperspectral classification models,the convergence speed of this model is faster,and it achieves significant improvement in classification performance with higher ground object classification accuracy.关键词
高光谱/图像分类/注意力机制/空间-光谱特征Key words
hyperspectral image/image classification/attention mechanism/spatial-spectral feature分类
信息技术与安全科学引用本文复制引用
刘和,宋璎珞,胡龙湘,刘国辉,王侃,王爱丽..基于空间金字塔注意力机制残差网络的高光谱图像分类[J].液晶与显示,2024,39(6):833-843,11.基金项目
国网黑龙江省电力公司科技项目(No.522411230008)Supported by State Grid Heilongjiang Electric Power Company Technology Project(No.522411230008) （No.522411230008）
