基于多层次空-谱融合网络的高光谱图像分类  

Hyperspectral image classification based on hierarchical spatial-spectral fusion network

作　　者：欧阳宁[1] 李祖锋 林乐平[1] OUYANG Ning;LI Zu-feng;LIN Le-ping(School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学信息与通信学院,广西桂林541004

出　　处：《吉林大学学报（工学版）》2022年第10期2438-2446,共9页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(62001133,62177012);广西科技重大专项项目(桂科AA20302001);广西科技基地和人才专项项目(桂科AD19110060);广西无线宽带通信与信号处理重点实验室基金项目(GXKL06200114);广西高等学校千名中青年骨干教师培育计划项目.

摘　　要：为了在高光谱图像分类中更好提取和表达光谱与空间的精细特征以及特征间的交互信息,提出一种基于多层次空-谱融合网络的高光谱图像分类方法。首先,利用多层次特征提取模块,分别提取高光谱图像的多层次空间和光谱特征;其次,设计空-谱特征交互融合模块将获得的多层次空间与光谱特征进行特征融合,以产生空-谱融合特征。本文方法可以结合网络中不同层次的空间与光谱特征,有效地捕获高光谱图像精细特征;同时,通过联合学习融合空间与光谱特征,捕获光谱与空间特征之间交互作用。实验结果表明,与现有基于神经网络的分类方法相比,所提出的高光谱图像分类算法能够获得更高的分类精度,表明该网络能有效地提取精细特征和增强空-谱融合特征的表达能力。To acquire fine spectral and spatial features and their interaction information in hyperspectral image classification,a hierarchical spatial-spectral fusion network is proposed.Firstly,the hierarchical features extraction module was exploited to extract the spectral and spatial features of hyperspectral image respectively.Secondly,the spatial-spectral feature interactive fusion module was designed and employed to fuse the features and produce the joint spatial-spectral features.The proposed network can not only extract and integrate the fine spatial and spectral features of different levels,but also capture the interaction between spectral and spatial features by joint learning.The experimental results show that the proposed network performed better than the state-of-the-art neural network-based classification methods.The network is shown the capability of extracting fine features and capturing the spatial-spectral joint features for classification.

关 键 词：高光谱图像分类 多层次特征提取模块 空-谱特征交互融合模块 特征融合 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]