[1]任媛媛,张显峰,马永建,等.基于卷积神经网络的无人机遥感影像农村建筑物目标检测[J].南京师范大学学报(工程技术版),2019,19(03):029.[doi:10.3969/j.issn.1672-1292.2019.03.005]
　Ren Yuanyuan,Zhang Xianfeng,Ma Yongjian,et al.Target Detection of Rural Buildings in UAV Remote Sensing ImagesBased on Convolutional Neural Network[J].Journal of Nanjing Normal University(Engineering and Technology),2019,19(03):029.[doi:10.3969/j.issn.1672-1292.2019.03.005]
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基于卷积神经网络的无人机遥感影像农村建筑物目标检测
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
19卷
期数:
2019年03期
页码:
029
栏目:
计算机工程
出版日期:
2019-09-30

文章信息/Info

Title:
Target Detection of Rural Buildings in UAV Remote Sensing ImagesBased on Convolutional Neural Network
文章编号:
1672-1292(2019)03-0029-08
作者:
任媛媛1; 2; 张显峰2; 3; 马永建1; 2; 杨启原1; 2; 汪传建1; 2; 戴建国1; 2; 赵庆展1; 2
(1.石河子大学信息科学与技术学院,新疆 石河子 832000)(2.国家遥感中心新疆兵团分部,新疆 石河子 832000)(3.北京大学遥感与地理信息系统研究所,北京 100871)
Author(s):
Ren Yuanyuan1; 2; Zhang Xianfeng2; 3; Ma Yongjian1; 2; Yang Qiyuan1; 2; Wang Chuanjian1; 2; Dai Jianguo1; 2; Zhao Qingzhan1; 2
Ren Yuanyuan1,2,Zhang Xianfeng2,3,Ma Yongjian1,2,Yang Qiyuan1,2,Wang Chuanjian1,2,Dai Jianguo1,2,Zhao Qingzhan1,2
关键词:
建筑物; 检测; 无人机; 深度学习; 卷积神经网络; Faster R-CNN
Keywords:
buildings; detection; UAV; deep learning; convolutional neural network; Faster R-CNN
分类号:
TP391
DOI:
10.3969/j.issn.1672-1292.2019.03.005
文献标志码:
A
摘要:
将深度学习应用于遥感影像目标识别,提出基于卷积神经网络的无人机遥感影像农村建筑物的目标检测方法,用端到端的方式训练Faster R-CNN网络模型,并应用于农村建筑物的快速精确识别. 该方法包括基于RPN网络的区域建议和基于Inception v2的卷积神经网络模型训练. 为了训练和测试模型,通过无人机采集南疆地区的农村建筑物遥感影像,并人工标注建立了农村建筑物的数据集,在TensorFlow深度学习框架上通过对该数据集目标检测验证了模型. 结果表明,基于改进的卷积神经网络目标检测方法对无人机遥感影像进行快速准确识别的总体精度超过90%,通过初始参数更新,模型收敛更快,对无人机遥感影像地物分类和目标识别具有一定的参考意义.
Abstract:
With deep learning applied to object recognition of remote sensing images,a method of object detection for rural buildings based on convolution neural network is proposed in the paper. The improved Faster R-CNN network model is trained in an end-to-end way and applied to rural buildings with the rapid and accurate identification. Specifically,the method mainly includes region recommendation based on RPN network and convolutional neural network model training based on Inception v2. In order to train and test the improved model,the remote sensing images of rural buildings in southern Xinjiang Region are collected by UAV,and the data set of rural buildings is established by manual labeling. Finally,the model is validated by the object detection of the data set with TensorFlow deep learning framework. Experimental results show that the overall accuracy of fast and accurate recognition of UAV remote sensing images based on the improved convolution neural network object detection method exceeds 90%. By updating the initial parameters,the model converges faster,which has a certain reference value for the classification and object recognition of UAV remote sensing images.

参考文献/References:

[1] 郑辛煜. 农村居民点遥感信息提取与时空演变规律研究[D]. 杭州:浙江大学,2017.
ZHENG X Y. Research on rural residential areas remote sensing information extraction and spatial-temporal evolution[D]. Hangzhou:Zhejiang University,2017.(in Chinese)
[2]刘扬,付征叶,郑逢斌. 高分辨率遥感影像目标分类与识别研究进展[J]. 地球信息科学学报,2015,17(9):1080-1091.
LIU Y,FU Z Y,ZHENG F B. Advances in target classification and recognition of high resolution remote sensing images[J]. Geo-information science,2015,17(9):1080-1091.(in Chinese)
[3]王金传,谭喜成,王召海,等. 基于Faster R-CNN深度网络的遥感影像目标识别方法研究[J]. 地球信息科学学报,2018,20(10):1500-1508.
WANG J Z,TAN X C,WANG Z H,et al. Faster R-CNN deep learning network based object recognition of remote sensing image[J]. Geo-information science,2018,20(10):1500-1508.(in Chinese)
[4]李小文. 汶川震灾中遥感的应急与反思[J]. 遥感学报,2008,12(6):838.
LI X W. Emergency response and reflection of remote sensing in Wenchuan earthquake disaster[J]. Journal of remote sensing,2008,12(6):838.(in Chinese)
[5]李德仁,李明. 无人机遥感系统的研究进展与应用前景[J]. 武汉大学学报(信息科学版),2014,39(5):505-513.
LI D R,LI M. Research progress and application prospect of UAV remote sensing system[J]. Geomatics and information science of Wuhan university,2014,39(5):505-513.(in Chinese)
[6]VIOLA P A,JONES M J. Rapid object detection using a boosted cascade of simple features[C]//Computer Society Conference on Computer Vision & Pattern Recognition. Kauai,USA:IEEE,2001.
[7]SCHULDT C,LAPTEV I,CAPUTO B. Recognizing human actions:a local SVM approach[C]//Proceedings of the 17th International Conference on Pattern Recognition. Cambridge,UK:IEEE,2004.
[8]FELZENSZWALB P F,MCALLESTER D A,RAMANAN D. A discriminatively trained,multiscale,deformable part model[C]//Conference on Computer Vision & Pattern Recognition. Anchorage,USA:IEEE,2008.
[9]SERMANET P,EIGEN D,ZHANG X,et al. OverFeat:integrated recognition,localization and detection using convolutional networks[C]//International Conference on Learning Representations. Banff,Canada:ICLR,2014.
[10]GIRSHICK R,DONAHUE J,DARRELL T,et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus,USA:IEEE,2014:580-587.
[11]HE K,ZHANG X,REN S,et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence,2015,37(9):1904-1916.
[12]GIRSHICK R. Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision. Santiago,Chile:IEEE,2015:1440-1448.
[13]REN S,HE K,GIRSHICK R,et al. Faster r-cnn:Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems. Montreal,Canada:NIPS,2015:91-99.
[14]REDMON J,DIVVALA S,GIRSHICK R,et al. You only look once:Unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,USA:IEEE,2016:779-788.
[15]LIU W,ANGUELOV D,ERHAN D,et al. Ssd:single shot multibox detector[C]//European Conference on Computer Vision. Amsterdam,Netherlands:ECCV,2016:21-37.
[16]曲景影,孙显,高鑫. 基于CNN模型的高分辨率遥感图像目标识别[J]. 国外电子测量技术,2016(8):45-50.
QU J Y,SUN X,GAO X. High resolution remote sensing image target recognition based on CNN Model[J]. Foreign electronic measurement technology,2016(8):45-50.(in Chinese)
[17]陈文康. 基于深度学习的农村建筑物遥感影像检测[J]. 测绘,2016,39(5):227-230.
CHEN W K. Remote sensing image detection of rural buildings based on deep learning[J]. Surveying and mapping,2016,39(5):227-230.(in Chinese)
[18]AUDEBERT N,SAUX B L,LEFèVRE S. Semantic segmentation of earth observation data using multimodal and multi-scale deep networks[C]//Asian Conference on Computer Vision. Taipei,China:AFCV,2016:180-196.
[19]SAITO S,YAMASHITA Y,AOKI Y. Multiple object extraction from aerial imagery with convolutional neural networks[J]. Electronic imaging,2016,60(1):10402-1/10402-9.
[20]金永涛,杨秀峰,高涛,等. 基于面向对象与深度学习的典型地物提取[J]. 国土资源遥感,2018,30(1):22-29.
JIN Y T,YANG X F,GAO T,et al. Typical ground object extraction based on object-oriented and deep learning[J]. Remote sensing for land & resources,2018,30(1):22-29.(in Chinese)
[21]刘文涛,李世华,覃驭楚. 基于全卷积神经网络的建筑物屋顶自动提取[J]. 地球信息科学学报,2018,20(11):1562-1570.
LIU W T,LI S H,QIN Y C. Object detection in high resolution remote sensing images based on fully convolution networks[J]. Geo-information science,2018,20(11):1562-1570.(in Chinese)
[22]UIJLINGS J R R,SANDE V D,et al. Selective search for object recognition[J]. International journal of computer vision,2013,104(2):154-171.
[23]LONG J,SHELHAMER E,DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston,USA:IEEE,2015:3431-3440.
[24]IOFFE S,SZEGEDY C. Batch normalization:Accelerating deep network training by reducing internal covariate shift[C]//International Conference on Machine Learning. Lille,France:ICML,2015.
[25]SZEGEDY C,VANHOUCKE V,LOFFE S,et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,USA:IEEE,2016:2818-2826.

备注/Memo

备注/Memo:
收稿日期:2019-07-05.
基金项目:国家重点研发计划(2017YFB0504203)、国家自然科学基金(41461088)、兵团科技计划(2016AB001、2017DB005).
通讯联系人:汪传建,博士,教授,研究方向:机器学习、数据挖掘. E-mail:wcj_inf@shzu.edu.cn
更新日期/Last Update: 2019-09-30