摘要
针对高光谱图像分类在特征提取过程中高分辨率信息丢失,导致分类精度下降的问题,提出一种基于空谱分组卷积密集网络的高光谱图像分类方法。设计光谱-空间三维分组卷积密集模块,对光谱与空间特征进行分步提取,利用分组卷积构造的密集网络能减少数据固有信息冗余,使高分辨率的特征进行重用,避免细节特征信息丢失;设计光谱残差注意力模块,该模块通过结合空-谱特征计算注意力权重,对提取到的光谱特征进行权重重分配,对光谱信息富有的区域进行增强。实验结果表明,相比于若干最优的深度网络方法,所提高光谱图像分类方法具有更好的分类性能。
Aiming at the issues of high-resolution information loss in the process of feature extraction in hyperspectral image classification,which leads to the decline of classification accuracy,a spatial-spectral group-convolution dense network was proposed.The spatial-spectral 3D group-convolution densenet module was exploited to extract the spectral and spatial features step by step.The information redundancy of hyperspectral data was reduced in the feature extraction process,the high-resolution features were reused through dense connection when using the dense network constructed by group-convolution,avoiding the loss of detailed feature information.The spectral residual attention module was designed and employed to calculate the attention weight combined with the spatial-spectral information,and to redistribute the weight of the extracted spectral features to enhance the area with rich spectral information.Experimental results show that the proposed network performs better than the state-of-the-art neural network-based classification methods.
作者
欧阳宁
李祖锋
林乐平
OUYANG Ning;LI Zu-feng;LIN Le-ping(Key Laboratory of Cognitive Radio and Information Processing of Ministry of Education,School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China;School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China)
出处
《计算机工程与设计》
北大核心
2022年第7期2031-2039,共9页
Computer Engineering and Design
基金
国家自然科学基金项目(62001133、61661017、61362021)
广西科技基地和人才专项基金项目(桂科AD19110060)
广西自然科学基金项目(2017GXNSFBA198212)
广西无线宽带通信与信号处理重点实验室基金项目(GXKL06200114)。
关键词
高光谱图像分类
三维分组卷积
密集网络
光谱残差注意力模块
空-谱特征
hyperspectral image classification
3D group-convolution
dense network
spectral residual attention module
spatial-spectral features
