Lightweight Fully-Connected Tensorial Mapping Network for Hyperspectral Image Classification

LIN Zhi-xin; ZHENG Yu-bang; MA Tian-yu; WANG Rui; LI Heng-chao

doi:10.12263/DZXB.20240477

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Lightweight Fully-Connected Tensorial Mapping Network for Hyperspectral Image Classification

PAPERS | 更新时间：2026-05-07

- Lightweight Fully-Connected Tensorial Mapping Network for Hyperspectral Image Classification
- ACTA ELECTRONICA SINICA Vol. 52, Issue 10, Pages: 3541-3551(2024)
- 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：
- 基金信息：
  
  National Natural Science Foundation of China(62271418)
- DOI：10.12263/DZXB.20240477
  CLC： TP751.1
- Received：27 May 2024，
  
  Revised：2024-09-11，
  
  Published：25 October 2024
- 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

RAYA-SERENO M D , ALONSO-AYUSO M , PANCORBO J L , et al . Residual effect and N fertilizer rate detection by high-resolution VNIR-SWIR hyperspectral imagery and solar-induced chlorophyll fluorescence in wheat [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 1 - 17 .

赵旭东 , 陶然 , 李伟 , 等 . 高光谱图像分数域信息提取理论与方法进展 [J ] . 电子学报 , 2022 , 50 ( 12 ): 2874 - 2883 .

ZHAO X D , TAO R , LI W , et al . Recent developments in fractional information extraction theory and methods of hyperspectral image [J ] . Acta Electronica Sinica , 2022 , 50 ( 12 ): 2874 - 2883 . (in Chinese)

HÄNSCH R , HELLWICH O . Fusion of multispectral LiDAR, hyperspectral, and RGB data for urban land cover classification [J ] . IEEE Geoscience and Remote Sensing Letters , 2021 , 18 ( 2 ): 366 - 370 .

方健 , 杨劲翔 , 肖亮 . 基于多层小波深度聚合网络的高光谱图像超分辨率方法 [J ] . 电子学报 , 2024 , 52 ( 1 ): 201 - 216 .

FANG J , YANG J X , XIAO L . MW-DAN: Multilevel wavelet-deep aggregation network for hyperspectral image super-resolution [J ] . Acta Electronica Sinica , 2024 , 52 ( 1 ): 201 - 216 . (in Chinese)

KHAN M H , SALEEM Z , AHMAD M , et al . Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants [J ] . Neural Computing and Applications , 2021 , 33 ( 21 ): 14507 - 14521 .

LI R , DUAN C . LiteDenseNet: A lightweight network for hyperspectral image classification [EB/OL ] . ( 2020-04-17 )[ 2024-05-08 ] . https://arxiv.org/abs/2004.08112 https://arxiv.org/abs/2004.08112 .

HUANG G , LIU Z , VAN DER MAATEN L , et al . Densely connected convolutional networks [C ] // 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2017 : 2261 - 2269 .

ZHANG X Y , ZHOU X Y , LIN M X , et al . ShuffleNet: An extremely efficient convolutional neural network for mobile devices [C ] // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 6848 - 6856 .

CUI B L , DONG X M , ZHAN Q Q , et al . LiteDepthwiseNet: A lightweight network for hyperspectral image classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 1 - 15 .

IOFFE S , SZEGEDY C . Batch normalization: Accelerating deep network training by reducing internal covariate shift [C ] // Proceedings of the 32nd International Conference on Machine Learning . Cambridge : JMLR , 2015 : 448 - 456

CHENG Y , WANG D , ZHOU P , et al . Model compression and acceleration for deep neural networks: The principles, progress, and challenges [J ] . IEEE Signal Processing Magazine , 2018 , 35 ( 1 ): 126 - 136 .

DENG L , LI G Q , HAN S , et al . Model compression and hardware acceleration for neural networks: A comprehensive survey [J ] . Proceedings of the IEEE , 2020 , 108 ( 4 ): 485 - 532 .

HU W S , LI H C , DENG Y J , et al . Lightweight tensor attention-driven ConvLSTM neural network for hyperspectral image classification [J ] . IEEE Journal of Selected Topics in Signal Processing , 2021 , 15 ( 3 ): 734 - 745 .

OSELEDETS I V . Tensor-train decomposition [J ] . SIAM Journal on Scientific Computing , 2011 , 33 ( 5 ): 2295 - 2317 .

MA T Y , LI H C , WANG R , et al . Lightweight tensorized neural networks for hyperspectral image classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 1 - 16 .

ZHAO Q , ZHOU G , XIE S , et al . Tensor ring decomposition [EB/OL ] . ( 2016-06-17 )[ 2024-05-08 ] . https://arxiv.org/pdf/1606.05535 https://arxiv.org/pdf/1606.05535 .

ZHENG Y B , HUANG T Z , ZHAO X L , et al . Fully-connected tensor network decomposition and its application to higher-order tensor completion [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2021 , 35 ( 12 ): 11071 - 11078 .

LI H C , LIN Z X , MA T Y , et al . Hybrid fully connected tensorized compression network for hyperspectral image classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2023 , 61 : 1 - 16 .

LEBEDEV V , GANIN Y , RAKHUBA M , et al . Speeding-up convolutional neural networks using fine-tuned CP-decomposition [EB/OL ] . ( 2015-04-24 )[ 2024-05-08 ] . https://arxiv.org/pdf/1412.6553 https://arxiv.org/pdf/1412.6553 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2016 : 770 - 778 .

Su J H , Li J L , Bhattacharjee B , et al . Tensorial neural networks: Generalization of neural networks and application to model compression [EB/OL ] . ( 2018-12-08 )[ 2024-05-08 ] . https://arxiv.org/pdf/1805.10352 https://arxiv.org/pdf/1805.10352 .

Grupo de Inteligencia Computacional de la Universidad del PAÍS Vasco . Hyperspectral remote sensing scenes [EB/OL ] . ( 2021-07-12 )[ 2024-05-08 ] . https://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_ Sensing_Scenes https://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes .

XU X D , LI W , RAN Q , et al . Multisource remote sensing data classification based on convolutional neural network [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 2 ): 937 - 949 .

CHANG C C , LIN C J . LIBSVM: A library for support vector machines [J ] . ACM Transactions on Intelligent Systems and Technology , 2011 , 2 ( 3 ): 1 - 27 .

PAOLETTI M E , HAUT J M , PLAZA J , et al . Deep learning classifiers for hyperspectral imaging: A review [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2019 , 158 : 279 - 317 .

ZHONG Z L , LI J , LUO Z M , et al . Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 2 ): 847 - 858 .

LI R , ZHENG S Y , DUAN C X , et al . Classification of hyperspectral image based on double-branch dual-attention mechanism network [J ] . Remote Sensing , 2020 , 12 ( 3 ): 582 - 606 .

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