液晶与显示2024，Vol.39Issue(6)：844-855,12.DOI:10.37188/CJLCD.2023-0158
基于多分支空谱特征增强的高光谱图像分类
Hyperspectral image classification based on multi-branch spatial-spectral feature enhancement
摘要
Abstract
To solve the problems of high noise interference in the hyperspectral image itself and the process of classification,insufficient extraction of spatial-spectral feature information,and poor classification performance under limited samples,a hyperspectral image classification model SSFE-MBACNN based on multi-branched spatial-spectral feature enhancement is proposed.First,shallow spatial-spectral feature information and deep spatial feature information are extracted separately using multi-branch feature extraction modules,and attention mechanism are introduced to suppress noise interference.Second,an improved fusion module for multi-scale spatial-spectral feature extraction and a spatial feature enhancement module combining dual pooling and dilated convolution are designed to achieve spatial-spectral feature enhancement,reduce the number of model parameters and improve classification performance.Finally,the global average pooling layer is used instead of the fully connected layer to further reduce the number of parameters and alleviate the model overfitting problem.The experimental results show that the overall classification accuracies of 0.990 7,0.997 5 and 0.994 7 are achieved for the Indian Pines(10%training sample),Pavia University(5%training sample)and Salinas(1%training sample)datasets.SSFE-MBACNN makes full use of the spatial-spectral feature information and achieves excellent classification performance with limited samples,which is significantly higher than other comparative methods.关键词
高光谱图像分类/特征增强/多分支特征提取/注意力机制/多尺度特征/双池化/空洞卷积Key words
hyperspectral image classification/feature enhancement/multi-branch feature extraction/attention mechanism/multi-scale features/dual pooling/dilated convolution分类
信息技术与安全科学引用本文复制引用
李铁,李文许,王军国,高乔裕..基于多分支空谱特征增强的高光谱图像分类[J].液晶与显示,2024,39(6):844-855,12.基金项目
辽宁省科技厅自然科学基金面上项目(No.2023-MS-314) （No.2023-MS-314）
辽宁省教育厅科学研究经费项目(No.LJ2020JCL007) （No.LJ2020JCL007）
辽宁省教育厅基本科研面上项目(No.LJKMZ20220678,No.LJKZ0357)Supported by Natural Science Foundation Project of Liaoning Science and Technology Department(No.2023-MS-314) （No.LJKMZ20220678,No.LJKZ0357）
Science and Technology Research Project of Liaoning Education Department(No.LJ2020JCL007) （No.LJ2020JCL007）
Fundamental Research Project of Liaoning Education Department(No.LJKMZ20220678,No.LJKZ0357) （No.LJKMZ20220678,No.LJKZ0357）
