基于轻量级全连接张量映射网络的高光谱图像分类方法

林知心; 郑玉棒; 马天宇; 王蕊; 李恒超

doi:10.12263/DZXB.20240477

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基于轻量级全连接张量映射网络的高光谱图像分类方法

学术论文 | 更新时间：2026-05-07

- 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
- 电子学报 2024年52卷第10期页码：3541-3551
- 作者机构：
  
  西南交通大学信息科学与技术学院，四川成都 611756
- 作者简介：
  
  [ "林知心女，1998年5月出生于福建省福州市.毕业于西南交通大学信息科学与技术学院.主要研究方向为张量分解、神经网络压缩和高光谱图像处理等.E-mail: zhixin_lin@163.com" ]
  [ "郑玉棒男，1993年11月出生于安徽省宿州市.现为西南交通大学信息科学与技术学院讲师、硕士生导师.主要研究方向包括高维信号与图像处理、张量建模与智能计算、机器学习及其数学理论等.E-mail: zhengyubang@163.com" ]
  [ "马天宇男，1996年7月出生于河北省邯郸市.现为西南交通大学信息科学与技术学院在读博士生.主要研究方向为张量分解、神经网络压缩和高光谱图像处理等.E-mai1: mty9678@my.swjtu.edu.cn" ]
  [ "王蕊女，1978年3月出生于甘肃省平凉市.现为西南交通大学信息科学与技术学院副教授、硕士生导师.主要研究方向为高光谱图像解混、遥感图像处理.E-mail: wangrui@swjtu.edu.cn" ]
  [ "李恒超男，1978年11月出生于山东省临沂市.现为西南交通大学信息科学与技术学院教授、博士生导师.主要研究方向为遥感图像处理与解译、计算智能与模式识别.中国电子学会会员编号：E190092013M.E-mail: hcli@swjtu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62271418)
- DOI：10.12263/DZXB.20240477
  中图分类号： TP751.1
- 收稿：2024-05-27，
  
  修回：2024-09-11，
  
  纸质出版：2024-10-25
- 稿件说明：
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林知心, 郑玉棒, 马天宇, 等. 基于轻量级全连接张量映射网络的高光谱图像分类方法[J]. 电子学报, 2024, 52(10): 3541-3551.

LIN Zhi-xin, ZHENG Yu-bang, MA Tian-yu, et al. Lightweight Fully-Connected Tensorial Mapping Network for Hyperspectral Image Classification[J]. Acta Electronica Sinica, 2024, 52(10): 3541-3551.
林知心, 郑玉棒, 马天宇, 等. 基于轻量级全连接张量映射网络的高光谱图像分类方法[J]. 电子学报, 2024, 52(10): 3541-3551. DOI：10.12263/DZXB.20240477

LIN Zhi-xin, ZHENG Yu-bang, MA Tian-yu, et al. Lightweight Fully-Connected Tensorial Mapping Network for Hyperspectral Image Classification[J]. Acta Electronica Sinica, 2024, 52(10): 3541-3551. DOI：10.12263/DZXB.20240477

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.

Abstract

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

Keywords

references

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