基于CNN的双边融合网络在高光谱图像分类中的应用

高红民; 曹雪莹; 杨耀; 花再军; 李臣明

doi:10.11959/j.issn.1000-436x.2020238

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基于CNN的双边融合网络在高光谱图像分类中的应用

学术论文 | 更新时间：2024-06-05

- 基于CNN的双边融合网络在高光谱图像分类中的应用
- Application of bilateral fusion model based on CNN in hyperspectral image classification
- 通信学报 2020年41卷第11期页码：132-140
- 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" ]
  [ "杨耀（1995- ），男，江西宜春人，河海大学硕士生，主要研究方向为深度学习与图像处理等" ]
  [ "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" ]
- 基金信息：
  
  国家自然科学基金资助项目(61701166);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);国家重点研发计划基金资助项目(2018YFC1508106)
- DOI：10.11959/j.issn.1000-436x.2020238
  中图分类号： TP302
- 网络出版日期：2020-11，
  
  纸质出版日期：2020-11-25
- 稿件说明：
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高红民, 曹雪莹, 杨耀, 等. 基于CNN的双边融合网络在高光谱图像分类中的应用[J]. 通信学报, 2020,41(11):132-140.

Hongmin GAO, Xueying CAO, Yao YANG, et al. Application of bilateral fusion model based on CNN in hyperspectral image classification[J]. Journal on communications, 2020, 41(11): 132-140.
高红民, 曹雪莹, 杨耀, 等. 基于CNN的双边融合网络在高光谱图像分类中的应用[J]. 通信学报, 2020,41(11):132-140. DOI： 10.11959/j.issn.1000-436x.2020238.

Hongmin GAO, Xueying CAO, Yao YANG, et al. Application of bilateral fusion model based on CNN in hyperspectral image classification[J]. Journal on communications, 2020, 41(11): 132-140. DOI： 10.11959/j.issn.1000-436x.2020238.

摘要

针对基于深度卷积神经网络的高光谱图像分类算法中存在的空间分辨率下降、池化操作引发特征丢失从而导致分类精度下降的问题，设计了一种由双边融合块构成的双边融合块网络。1×1卷积与超链接构成双边融合块上结构，传递局部空间特征，池化、卷积、反卷积、上采样组成下结构，强化高效判别特征。在3个基准高光谱图像数据集上的实验结果表明，该模型优于其他同类分类模型。

Abstract

Aiming at the issues of decreasing spatial resolution and feature loss caused by pooling operation in depth CNN-based hyperspectral image classification algorithm

a bilateral fusion block network （DFBN）composed of bilateral fusion blocks was designed.The upper structure of bilateral fusion block was constituted by 1×1 convolution and hyperlink

which was used to transfer local spatial characteristics.The lower structure was constituted by pooling layer

convolutional layer

deconvolution layer and upsampling to enhance the characteristics of efficient discrimination.Experimental results on three benchmark hyperspectral image data sets illustrate that the model is superior to other similar classification models.

关键词

Keywords

references

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