基于多尺度近端特征拼接网络的高光谱图像分类方法

高红民; 曹雪莹; 陈忠昊; 花再军; 李臣明; 陈月

doi:10.11959/j.issn.1000-436x.2021024

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基于多尺度近端特征拼接网络的高光谱图像分类方法

学术论文 | 更新时间：2024-06-05

- 基于多尺度近端特征拼接网络的高光谱图像分类方法
- Hyperspectral image classification method based on multi-scale proximal feature concatenate network
- 通信学报 2021年42卷第2期页码：92-102
- 作者机构：
  
  河海大学计算机与信息学院，江苏南京 211100
- 作者简介：
  
  [ "高红民（1983- ），男，江苏仪征人，博士，河海大学教授，主要研究方向为深度学习等智能计算方法、图像大数据与人工智能、遥感影像处理等。" ]
  [ "曹雪莹（1994- ），女，江苏睢宁人，河海大学硕士生，主要研究方向为深度学习与图像处理等。" ]
  [ "陈忠昊（1997- ），男，安徽马鞍山人，河海大学硕士生，主要研究方向为深度学习与高光谱图像处理等。" ]
  [ "花再军（1983- ），男，江苏姜堰人，河海大学实验师，主要研究方向为机器学习及图像处理等。" ]
  [ "李臣明（1969- ），男，内蒙古通辽人，河海大学教授，主要研究方向为智能信息处理、遥感技术与系统等。" ]
  [ "陈月（1995- ），女，江苏扬州人，河海大学硕士生，主要研究方向为高光谱遥感影像分类。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62071168);国家重点研发计划基金资助项目(2018YFC1508106);中央高校基本科研业务费基金资助项目(B200202183);江苏省研究生科研与实践创新计划基金资助项目(SJCX20_0181)
- DOI：10.11959/j.issn.1000-436x.2021024
  中图分类号： TP302
- 网络首发：2021-02，
  
  纸质出版：2021-02-25
- 稿件说明：
移动端阅览
高红民, 曹雪莹, 陈忠昊, 等. 基于多尺度近端特征拼接网络的高光谱图像分类方法[J]. 通信学报, 2021,42(2):92-102.

Hongmin GAO, Xueying CAO, Zhonghao CHEN, et al. Hyperspectral image classification method based on multi-scale proximal feature concatenate network[J]. Journal on Communications, 2021, 42(2): 92-102.
高红民, 曹雪莹, 陈忠昊, 等. 基于多尺度近端特征拼接网络的高光谱图像分类方法[J]. 通信学报, 2021,42(2):92-102. DOI： 10.11959/j.issn.1000-436x.2021024.

Hongmin GAO, Xueying CAO, Zhonghao CHEN, et al. Hyperspectral image classification method based on multi-scale proximal feature concatenate network[J]. Journal on Communications, 2021, 42(2): 92-102. DOI： 10.11959/j.issn.1000-436x.2021024.

摘要

针对基于传统卷积神经网络模型的高光谱图像分类算法细节表现力不强及网络结构过于复杂的问题，设计了一种基于多尺度近端特征拼接网络的高光谱图像分类方法。通过引入多尺度滤波器和空洞卷积，在保持模型轻量化的同时可以获取更丰富的空间-光谱判别特征，并提出利用卷积神经网络近端特征间的相互联系进一步增强细节表现力。在3个基准高光谱图像数据集上的实验结果表明，所提方法优于其他分类模型。

Abstract

Aiming at the phenomenon that the hyperspectral classification algorithm based on traditional CNN model was not expressive enough in detail and the network structure was too complex

a hyperspectral image classification method based on multi-scale proximal feature concatenate network (MPFCN) was designed.By introducing multi-scale filter and cavity convolution

the model could be kept light and the discriminative features of the space spectrum could be obtained

and the correlation between the proximal features of the CNN was proposed to further enhance the detail expression.Experimental results on three benchmark hyperspectral image data sets show that the proposed method is superior to other classification models.

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references

ZHANG L F , ZHANG L P , TAO D C , et al . Hyperspectral remote sensing image subpixel target detection based on supervised metric learning [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2014 , 52 ( 8 ): 4955 - 4965 .

张亚光 , 陈建平 , 李诗 . 高光谱遥感在土壤重金属污染监测中的应用 [J ] . 地质学刊 , 2019 , 43 ( 3 ): 491 - 498 .

ZHANG Y G , CHEN J P , LI S . Application of hyper-spectral remote sensing in soil heavy metal pollution monitoring [J ] . Journal of Geology , 2019 , 43 ( 3 ): 491 - 498 .

王新忠 , 卢青 , 张晓东 , 等 . 基于高光谱图像的黄瓜种子活力无损检测 [J ] . 江苏农业学报 , 2019 , 35 ( 5 ): 1197 - 1202 .

WANG X Z , LU Q , ZHANG X D , et al . Non-destructive detection of cucumber seeds vigor based on hyperspectral imaging [J ] . Jiangsu Journal of Agricultural Sciences , 2019 , 35 ( 5 ): 1197 - 1202 .

SANDIDGE J C , HOLYER R J . Coastal bathymetry from hyperspectral observations of water radiance [J ] . Remote Sensing of Environment , 1998 , 65 ( 3 ): 341 - 352 .

SAMANIEGO L , BARDOSSY A , SCHULZ K . Supervised classification of remotely sensed imagery using a modified K-NN technique [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2008 , 46 ( 7 ): 2112 - 2125 .

肖博林 . 基于支持向量机的高光谱遥感影像分类 [J ] . 科技创新与应用 , 2020 ( 4 ): 22 - 24 .

XIAO B L . Hyperspectral remote sensing image classification based on support vector machin [J ] . Technology Innovation and Application , 2020 ( 4 ): 22 - 24 .

WANG Q , LIN J Z , YUAN Y . Salient band selection for hyperspectral image classification via manifold ranking [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2016 , 27 ( 6 ): 1279 - 1289 .

LICCIARDI G , MARPU P R , CHANUSSOT J , et al . Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles [J ] . IEEE Geoscience and Remote Sensing Letters , 2012 , 9 ( 3 ): 447 - 451 .

GAO W , PENG Y . Ideal kernel-based multiple kernel learning for spectral-spatial classification of hyperspectral image [J ] . IEEE Geoscience and Remote Sensing Letters , 2017 , 14 ( 7 ): 1051 - 1055 .

CHEN Y , NASRABADI N M , TRAN T D . Hyperspectral image classification via kernel sparse representation [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2013 , 51 ( 1 ): 217 - 231 .

LECUN Y , BENGIO Y , HINTON G . Deep learning [J ] . Nature , 2015 , 521 ( 7553 ): 436 - 444 .

CHEN Y S , LIN Z H , ZHAO X , et al . Deep learning-based classification of hyperspectral data [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2014 , 7 ( 6 ): 2094 - 2107 .

LIU P , ZHANG H , EOM K B . Active deep learning for classification of hyperspectral images [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2017 , 10 ( 2 ): 712 - 724 .

CHEN Y S , JIANG H L , LI C Y , et al . Deep feature extraction and classification of hyperspectral images based on convolutional neural networks [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2016 , 54 ( 10 ): 6232 - 6251 .

LI Y , ZHANG H K , SHEN Q , et al . Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network [J ] . Remote Sensing , 2017 , 9 ( 1 ): 67 .

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 . Piscataway:IEEE Press , 2016 : 770 - 778 .

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 .

SONG W W , LI S T , FANG L Y , et al . Hyperspectral image classification with deep feature fusion network [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 6 ): 3173 - 3184 .

WANG W J , DOU S G , JIANG Z M , et al . A fast dense spectral-spatial convolution network framework for hyperspectral images classification [J ] . Remote Sensing , 2018 , 10 ( 7 ): 1068 .

王莹 . 改进的基于 CNN 的高光谱遥感图像分类办法 [J ] . 现代商贸工业 , 2019 , 40 ( 35 ): 204 - 206 .

WANG Y . Improved hyperspectral remote sensing image classification method based on CNN [J ] . Modern Business Trade Industry , 2019 , 40 ( 35 ): 204 - 206 .

SZEGEDY C , LIU W , JIA Y Q , et al . Going deeper with convolutions [C ] // 2015 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2015 : 1 - 9 .

LEE H , KWON H . Going deeper with contextual CNN for hyperspectral image classification [J ] . IEEE Transactions on Image Processing , 2017 , 26 ( 10 ): 4843 - 4855 .

ZHANG C J , LI G D , DU S H . Multi-scale dense networks for hyperspectral remote sensing image classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 11 ): 9201 - 9222 .

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