[1]韩文军,孙小虎,吉根林,等.基于卷积神经网络的多光谱与全色遥感图像融合算法[J].南京师大学报(自然科学版),2021,44(03):123-130.[doi:10.3969/j.issn.1001-4616.2021.03.018]
　Han Wenjun,Sun Xiaohu,Ji Genlin,et al.Multispectral and Panchromatic Remote Sensing Image Fusion AlgorithmBased on Convolutional Neural Networks[J].Journal of Nanjing Normal University(Natural Science Edition),2021,44(03):123-130.[doi:10.3969/j.issn.1001-4616.2021.03.018]
点击复制

基于卷积神经网络的多光谱与全色遥感图像融合算法()
分享到：

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第44卷
期数:
2021年03期
页码:
123-130
栏目:
·计算机科学与技术·
出版日期:
2021-09-15

文章信息/Info

Title:
Multispectral and Panchromatic Remote Sensing Image Fusion AlgorithmBased on Convolutional Neural Networks
文章编号:
1001-4616(2021)03-0123-08
作者:
韩文军1; 孙小虎1; 吉根林2; 苏晓云3; 谢 非3; 吴 冰4; 陈 红5
(1.国家电网经济技术研究院有限公司,北京 102209)(2.南京师范大学计算机与电子信息学院,江苏 南京 210023)(3.南京师范大学南瑞电气与自动化学院,江苏 南京 210023)(4.浙江省电力有限公司经济技术研究院,浙江 杭州 310000)(5.江苏省电力有限公司经济技术研究院,江苏 南京 210008)
Author(s):
Han Wenjun1; Sun Xiaohu1; Ji Genlin2; Su Xiaoyun3; Xie Fei3; Wu Bing4; Chen Hong5
(1.State Grid Economic and Technology Research Institute Co. Ltd,Beijing 102209,China)(2.School of Computer and Electronic Information,Nanjing Normal University,Nanjing 210023,China)(3.School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210023,China)(4.Economic and Technology Research Institute of Zhejiang Electric Power Co. Ltd,Hangzhou 310000,China)(5.Economic and Technology Research Institute of Jiangsu Electric Power Co. Ltd,Nanjing 210008,China)
关键词:
卷积神经网络; 多光谱图像; 全色图像; 融合算法
Keywords:
convolutional neural networks; multispectral image; panchromatic image; fusion algorithm
分类号:
TP751
DOI:
10.3969/j.issn.1001-4616.2021.03.018
文献标志码:
A
摘要:
地理信息数据具有不同分辨率、不同精度、不同覆盖范围等多尺度特征,在应用中往往需要同时具备高光谱分辨率和高空间分辨率两种信息的图像. 为提高多光谱图像和全色图像融合的准确性,提出了基于卷积神经网络的多光谱与全色遥感图像融合算法. 首先,对训练集内图像进行预处理,构建适用于本算法的图像数据集; 然后,拓展卷积神经网络卷积层,提高三通道关联性,提取更多图像信息; 最后,使用深度可分离卷积神经网络,提高融合图像的精度的同时,也加快了融合速度. 在Downsampled ImageNet数据集提供的原始图像上对算法融合准确度和速度进行了验证. 实验结果表明,相较于传统卷积神经网络算法,本文算法均方误差降低7%,融合时间减少了29%,具有较好的融合效果.
Abstract:
The geographic information data has multi-scale characteristics such as different resolution,different precision and different coverage. In application,it is often necessary to have both high spectral resolution and high spatial resolution images. In order to improve the accuracy of multispectral image and panchromatic image fusion,a fusion algorithm for multispectral and panchromatic remote sensing images based on convolutional neural networks is proposed. Firstly,the images in the training set is preprocessed to construct the image data set suitable for the algorithm. Secondly,the convolution layers of convolutional neural networks are expanded to improve the three channel correlation and extract more image information. Finally,the depth separable convolutional neural networks are used to improve the accuracy of the fusion image,which can also accelerate the fusion speed. The accuracy and speed of the algorithm are verified using the original images provided by downsampled ImageNet dataset. Experimental results show that the mean square error of the proposed algorithm is 7% lower and the fusion time is reduced by 29% compared with the traditional convolutional neural networks method,and obtain a good fusion effect.

参考文献/References:

[1] 李昌洁,宋慧慧,张开华,等. 条件生成对抗遥感图像时空融合[J]. 中国图象图形学报,2021,26(3):714-726.
[2]李树涛,李聪妤,康旭东. 多源遥感图像融合发展现状与未来展望[J]. 遥感学报,2021,25(1):148-166.
[3]黄波,姜晓璐. 增强型空间像元分解时空遥感影像融合算法[J]. 遥感学报,2021,25(1):241-250.
[4]顾宇鑫,马小虎. 采用稀疏变换和拉普拉斯金字塔的数字水印算法[J]. 计算机辅助设计与图形学学报,2018,30(5):901-910.
[5]傅志中,王雪,李晓峰,等. 基于视觉显著性和NSCT的红外与可见光图像融合[J]. 电子科技大学学报,2017,46(2):357-362.
[6]殷明,段普宏,褚标,等. 基于非下采样双树复轮廓波变换和稀疏表示的红外和可见光图像融合[J]. 光学精密工程,2016,24(7):1763-1771.
[7]刘先红,陈志斌,秦梦泽. 结合引导滤波和卷积稀疏表示的红外与可见光图像融合[J]. 光学精密工程,2018,26(5):1242-1253.
[8]张建明,邱晓晖. 基于Curvelet变换的指纹图像去噪[J]. 计算机技术与发展,2018,28(5):164-167.
[9]颜正恕,王璟. 基于非下采样轮廓波变换耦合对比度特征的遥感图像融合算法[J]. 电子测量与仪器学报,2020,34(3):28-35.
[10]GHASSEMIAN H. A review of remote sensing image fusion methods[J]. Information fusion,2016,32:75-89.
[11]FAN C,WANG L,LIU P,et al. Compressed sensing based remote sensing image reconstruction via employing similarities of reference images[J]. Multimedia tools and applications,2016,75(19):12201-12225.
[12]LI C M,ZHANG H G,WU P D,et al. A complex junction recognition method based on GoogLeNet model[J]. Transactions in GIS,2020,24(6):1756-1778.
[13]Srivastava R K,Greff K,Schmidhuber J. Training very deep networks[C]//Proceedings of the Advances in Neural Information Processing Systems. Montreal,Canada:MIT Press,2015:2377-2385.
[14]许可,高尚. 深度卷积神经网络LeNet-5和ResNet的对比以及应用分析[J]. 电子设计工程,2020,28(2):82-85.
[15]马永杰,刘培培. 基于DenseNet进化卷积神经网络的图像分类算法[J]. 激光与光电子学进展,2020,57(24):1-5.
[16]GANGKOFNER U G,PRADHAN P S,HOLCOMB D W. Optimizing the high-pass filter addition technique for image fusion[J]. Photogrammetric engineering & remote sensing,2007,73(9):1107-1118.
[17]李红,刘芳,杨淑媛,等. 基于深度支撑值学习网络的遥感图像融合[J]. 计算机学报,2016,39(8):1583-1596.

相似文献/References:

[1]王 征,李皓月,许洪山,等.基于卷积神经网络和SVM的中国画情感分类[J].南京师大学报(自然科学版),2017,40(03):74.[doi:10.3969/j.issn.1001-4616.2017.03.011]
　Wang Zheng,Li Haoyue,Xu Hongshan,et al.Chinese Painting Emotion Classification Based onConvolution Neural Network and SVM[J].Journal of Nanjing Normal University(Natural Science Edition),2017,40(03):74.[doi:10.3969/j.issn.1001-4616.2017.03.011]
[2]方谦昊,朱 红,何瀚志,等.基于卷积神经网络的脑膜瘤亚型影像自动分级[J].南京师大学报(自然科学版),2018,41(03):22.[doi:10.3969/j.issn.1001-4616.2018.03.004]
　Fang Qianhao,Zhu Hong,He Hanzhi,et al.Automatic Classification of Meningioma Subtype ImageBased on Convolutional Neural Network[J].Journal of Nanjing Normal University(Natural Science Edition),2018,41(03):22.[doi:10.3969/j.issn.1001-4616.2018.03.004]
[3]郑 冬,李向群,许新征.基于轻量化SSD的车辆及行人检测网络[J].南京师大学报(自然科学版),2019,42(01):73.[doi:10.3969/j.issn.1001-4616.2019.01.012]
　Zheng Dong,Li Xiangqun,Xu Xinzheng.Vehicle and Pedestrian Detection Model Based on Lightweight SSD[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):73.[doi:10.3969/j.issn.1001-4616.2019.01.012]
[4]尤鸣宇,韩 煊.基于样本扩充的小样本车牌识别[J].南京师大学报(自然科学版),2019,42(03):1.[doi:10.3969/j.issn.1001-4616.2019.03.001]
　You Mingyu,Han Xuan.Small Sample License Plate Recognition Based on Sample Expansion[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):1.[doi:10.3969/j.issn.1001-4616.2019.03.001]
[5]赵文芳,林润生,唐 伟,等.基于深度学习的PM2.5短期预测模型[J].南京师大学报(自然科学版),2019,42(03):32.[doi:10.3969/j.issn.1001-4616.2019.03.005]
　Zhao Wenfang,Lin Runsheng,Tang Wei,et al.Forecasting Model of Short-Term PM2.5 ConcentrationBased on Deep Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):32.[doi:10.3969/j.issn.1001-4616.2019.03.005]
[6]马晓慧,马尚才,闫俊伢,等.基于距离感知的目标情感分类模型[J].南京师大学报(自然科学版),2021,44(04):111.[doi:10.3969/j.issn.1001-4616.2021.04.014]
　Ma Xiaohui,Ma Shangcai,Yan Junya,et al.Distance-Based Model for Target-Level Sentiment Analysis[J].Journal of Nanjing Normal University(Natural Science Edition),2021,44(03):111.[doi:10.3969/j.issn.1001-4616.2021.04.014]
[7]钟桂凤,庞雄文,孙道宗.基于差分进化的卷积神经网络的文本分类研究[J].南京师大学报(自然科学版),2022,45(01):136.[doi:10.3969/j.issn.1001-4616.2022.01.019]
　Zhong Guifeng,Pang Xiongwen,Sun Daozong.Research on Text Classification Based on Convolutional Neural Network of Differential Evolution[J].Journal of Nanjing Normal University(Natural Science Edition),2022,45(03):136.[doi:10.3969/j.issn.1001-4616.2022.01.019]
[8]邬忠萍,刘新厂,郝宗波.基于并行CNN和识别策略优化的车牌识别方法研究[J].南京师大学报(自然科学版),2023,46(03):98.[doi:10.3969/j.issn.1001-4616.2023.03.013]
　Wu Zhongping,Liu Xinchang,Hao Zongbo.Research of License Plate Recognition Method Based on Parallel CNN and Optimization Strategies[J].Journal of Nanjing Normal University(Natural Science Edition),2023,46(03):98.[doi:10.3969/j.issn.1001-4616.2023.03.013]

备注/Memo

备注/Memo:
收稿日期:2020-06-11.
基金项目:国家电网公司科技项目(5200-201956106A-0-0-00).
通讯作者:吉根林,博士,教授,博士生导师,研究方向:大数据分析与挖掘技术. E-mail:glji@njnu.edu.cn; 谢非,博士,副教授,研究方向:机器视觉与深度学习. E-mail:xiefei@njnu.edu.cn
更新日期/Last Update: 2021-09-15