基于轻量级全连接张量映射网络的高光谱图像分类方法  

Lightweight Fully-Connected Tensorial Mapping Network for Hyperspectral Image Classification

作　　者：林知心 郑玉棒 马天宇 王蕊[1] 李恒超[1] LIN Zhi-xin;ZHENG Yu-bang;MA Tian-yu;WANG Rui;LI Heng-chao(School of Information Science and Technology,Southwest Jiaotong University,Chengdu,Sichuan 611756,China)

机构地区：[1]西南交通大学信息科学与技术学院,四川成都611756

出　　处：《电子学报》2024年第10期3541-3551,共11页Acta Electronica Sinica

基　　金：国家自然科学基金(No.62271418)。

摘　　要：近年来,基于卷积神经网络的深度学习模型已经在高光谱图像分类领域取得优异表现.然而,模型性能的提升通常依赖于更深、更宽的网络结构,导致参数量和计算量增长,从而限制了模型在机载或星载载荷中的实际部署.为此,本文提出基于轻量级全连接张量映射网络的高光谱图像分类方法.根据全连接张量网络分解的映射思想以及高光谱图像“图谱合一”的结构特点,本文设计两种张量映射卷积单元,通过使用多个具有全连接结构的小尺寸卷积核代替原始卷积核,降低了卷积层的时间和空间复杂度.此外,基于新单元构建残差双分支张量模块.双分支结构共享同一组权重参数,并采用通道分割操作减少特征通道数,提升特征提取过程的实时性.本文所提模型通过使用新单元和新模块充分挖掘高光谱图像的局部空谱信息和全局光谱信息,有效提高了分类性能并减少硬件资源消耗.在三个常用高光谱图像数据集上的实验结果表明,所提模型相较于其他现有工作具有更高的分类性能以及更低的参数量和计算量.In recent years,convolutional neural networks have demonstrated outstanding performance in HSIC(Hyperspectral Image Classification).However,the improvement of model performance involves adopting deeper and broader network architectures,leading to an increased number of parameters and operations,thus hindering deployment in airborne or on-board devices.To this end,this paper introduces a HSIC method based on the LiteFCTMN(Lightweight Fully-Connected Tensorial Mapping Network).We design two convolutional units based on the mapping way of FCTN(Fully-Connected Tensor Network)decomposition and the structural characteristics of HSIs.By mapping the original convolution kernel to multiple small-sized convolution kernels with fully-connected structures,the complexity of the novel units is reduced while their expressiveness is improved.In addition,the RDT(Residual Double-Branch Tensorial)module is constructed using the designed units.In this module,two branches share the same weights,and a channel split operation is employed to reduce the number of feature channels,thereby reducing complexity.The proposed model strategically leverages both local spatial-spectral information from RDT and global spectral information from the new units,resulting in enhanced classification performance and reduced hardware consumption.Experimental results on three widely used HSI datasets demonstrate that the proposed model achieves superior classification performance and lower complexity compared to the state-of-the-art works.

关 键 词：高光谱图像分类 模型压缩 全连接张量网络分解 卷积神经网络 张量神经网络 轻量卷积模块 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]