基于全卷积神经网络的灌区无人机正射影像渠系提取
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摘要
为快速准确获取灌区渠系分布信息,科学调配区域农业水资源、提高水资源利用率,通过基于全卷积神经网络(Fully convolutional networks,FCN)的语义分割模型进行渠系轮廓提取。利用无人机采集正射影像并进行标注,以VGG-19网络为基础,通过多尺度特征融合的方式实现FCN-8s结构,使用Tensorflow深度学习框架构建FCN渠系提取模型;对数据集进行数据增强,分割后放入FCN模型中训练、测试。实验结果显示,针对不同复杂程度的测试区域,FCN模型的提取准确度、完整度、精度均高于支持向量机方法和改进霍夫变换方法,均值分别为95. 78%、92. 29%、89. 45%。结果表明,该方法能够实现灌区渠系轮廓的高精度提取,具有较好的泛化性和鲁棒性。
The distribution information of irrigation networks in irrigation area acquired quickly and accurately had great important research significance,especially in the scientific allocation of regional agricultural water resources and improvement of water resources utilization rate. The semantic segmentation model based on fully convolutional networks( FCN) was used to extract the irrigation networks contours. Firstly,the orthophotos collected by UAV were manually labeled. Based on the VGG-19 network,the FCN-8 s structure was realized by multi-scale feature fusion,and the Tensorflow deep learning framework was used to construct the FCN irrigation networks extraction model. Secondly,the data sets were enhanced and segmented. Lastly,the data sets were put into the FCN model for training and testing. The experimental results showed that for the test areas with different complexities,the extraction precision,completion and accuracy of the FCN model were 95. 78%, 92. 29% and 89. 45%,respectively,which were higher than the support vector machine( SVM) method and the revised Hough transform( RHT) method. The results showed that the method can achieve high-accuracy extraction of the irrigation networks contours in irrigation area,and had good generalization and robustness,which provided good technical support for further accurate irrigation in agriculture.
The distribution information of irrigation networks in irrigation area acquired quickly and accurately had great important research significance,especially in the scientific allocation of regional agricultural water resources and improvement of water resources utilization rate. The semantic segmentation model based on fully convolutional networks( FCN) was used to extract the irrigation networks contours. Firstly,the orthophotos collected by UAV were manually labeled. Based on the VGG-19 network,the FCN-8 s structure was realized by multi-scale feature fusion,and the Tensorflow deep learning framework was used to construct the FCN irrigation networks extraction model. Secondly,the data sets were enhanced and segmented. Lastly,the data sets were put into the FCN model for training and testing. The experimental results showed that for the test areas with different complexities,the extraction precision,completion and accuracy of the FCN model were 95. 78%, 92. 29% and 89. 45%,respectively,which were higher than the support vector machine( SVM) method and the revised Hough transform( RHT) method. The results showed that the method can achieve high-accuracy extraction of the irrigation networks contours in irrigation area,and had good generalization and robustness,which provided good technical support for further accurate irrigation in agriculture.
引文
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[2]屈忠义,杨晓,黄永江.内蒙古河套灌区节水工程改造效果分析与评估[J/OL].农业机械学报,2015,46(4):70-76,112.QU Zhongyi,YANG Xiao,HUANG Yongjiang. Analysis and assessment of water-saving project of Hetao irrigation district in Inner Mongolia[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(4):70-76,112. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20150412&flag=1&journal_id=jcsam. DOI:10. 6041/j.issn. 1000-1298. 2015. 04. 012.(in Chinese)
[3] ZHANG H,YANG J,BAARTMAN J E,et al. Quality of terrestrial data derived from UAV photogrammetry:a case study of Hetao irrigation district in northern China[J]. International Journal of Agricultural and Biological Engineering,2018,11(3):171-177.
[4]张海鑫.基于无人机遥感的渠系分布信息提取方法研究[D].杨凌:西北农林科技大学,2016.ZHANG Haixin. Method of extracting canal distribution based on UAV remote sensing of system[D]. Yangling:Northwest A&F University,2016.(in Chinese)
[5] LIU X,ZHANG Z. Drainage network extraction using Li DAR-derived DEM in volcanic plains[J]. Area,2011,43(1):42-52.
[6]刘如意,宋建锋,权义宁,等.一种自动的高分辨率遥感影像道路提取方法[J].西安电子科技大学学报,2017,44(1):100-105.LIU Ruyi,SONG Jianfeng,QUAN Yining,et al. Automatic road extraction method for high-resolution remote sensing images[J].Journal of Xidian University,2017,44(1):100-105.(in Chinese)
[7]汪小钦,王苗苗,王绍强,等.基于可见光波段无人机遥感的植被信息提取[J].农业工程学报,2015,31(5):152-159.WANG Xiaoqin,WANG Miaomiao,WANG Shaoqiang,et al. Vegetation information extraction based on remote sensing of visible light band UAV[J]. Transactions of the CSAE,2015,31(5):152-159.(in Chinese)
[8]刘凯,汤国安,黄骁力,等.面向地形特征的DEM与影像纹理差异分析[J].地球信息科学学报,2016,18(3):386-395.LIU Kai,TANG Guoan,HUANG Xiaoli,et al. DEM and image texture differences for terrain features[J]. Journal of Earth Information Science,2016,18(3):386-395.(in Chinese)
[9]张宏鸣,李瑶,王猛,等.基于无人机DEM的灌区渠系提取方法[J/OL].农业机械学报,2017,48(10):165-171.ZHANG Hongming,LI Yao,WANG Meng,et al. Extraction method of irrigation networks in irrigated area based on UAV DEM[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(10):165-171. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? flag=1&file_no=20171020&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298.2017. 10. 020.(in Chinese)
[10]刘焕军,杨昊轩,徐梦园,等.基于裸土期多时相遥感影像特征及最大似然法的土壤分类[J].农业工程学报,2018,34(14):132-139.LIU Huanjun,YANG Haoxuan,XU Mengyuan,et al. Soil classification based on maximum likelihood method and features of multi-temporal remote sensing images in bare soil period[J]. Transactions of the CSAE,2018,34(14):132-139.(in Chinese)
[11]赵越,周萍.改进的K-means算法在遥感图像分类中的应用[J].国土资源遥感,2011,23(2):87-90.ZHAO Yue,ZHOU Ping. An improved K-means algorithm for remote sensing classification[J]. Remote Sensing for Land&Resources,2011,23(2):87-90.(in Chinese)
[12]王振武,孙佳骏,于忠义,等.基于支持向量机的遥感图像分类研究综述[J].计算机科学,2016,43(9):11-17.WANG Zhenwu,SUN Jiajun,YU Zhongyi,et al. Review of remote sensing image classification based on support vector machine[J]. Computer Science,2016,43(9):11-17.(in Chinese)
[13]张宏鸣,任强,韩文霆,等.基于SVM的灌区无人机影像渠系提取[J/OL].农业机械学报,2018,49(2):141-148.ZHANG Hongming,REN Qiang,HAN Wenting,et al. Extraction of irrigation networks of UAV orthophotos based on SVM classification method[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(2):141-148. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20180219&flag=1&journal_id=jcsam. DOI:10. 6041/j.issn. 1000-1298. 2018. 02. 019.(in Chinese)
[14]刘德营,王家亮,林相泽,等.基于卷积神经网络的白背飞虱识别方法[J/OL].农业机械学报,2018,49(5):51-56.LIU Deying,WANG Jialiang,LIN Xiangze,et al. Automatic identification method for Sogatella furcifera based on convolutional neural network[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):51-56. http:∥www. jcsam. org/jcsam/ch/reader/view_abstract. aspx? flag=1&file_no=20180506&journal_id=jcsam. DOI:10. 6041/j. issn.1000-1298. 2018. 05. 006.(in Chinese)
[15]卢伟,胡海阳,王家鹏,等.基于卷积神经网络面部图像识别的拖拉机驾驶员疲劳检测[J].农业工程学报,2018,34(7):192-199.LU Wei,HU Haiyang,WANG Jiapeng,et al. Tractor driver fatigue detection based on convolution neural network and facial image recognition[J]. Transactions of the CSAE,2018,34(7):192-199.(in Chinese)
[16]龙满生,欧阳春娟,刘欢,等.基于卷积神经网络与迁移学习的油茶病害图像识别[J].农业工程学报,2018,34(18):194-201.LONG Mansheng,OUYANG Chunjuan,LIU Huan,et al. Image recognition of Camellia oleifera diseases based on convolutional neural network&transfer learning[J]. Transactions of the CSAE,2018,34(18):194-201.(in Chinese)
[17]杨国国,鲍一丹,刘子毅.基于图像显著性分析与卷积神经网络的茶园害虫定位与识别[J].农业工程学报,2017,33(6):156-162.YANG Guoguo,BAO Yidan,LIU Ziyi. Localization and recognition of pests in tea plantation based on image saliency analysis and convolutional neural network[J]. Transactions of the CSAE,2017,33(6):156-162.(in Chinese)
[18]李政,李永树,吴玺,等.基于卷积神经网络的空心村高分影像建筑物检测方法[J/OL].农业机械学报,2017,48(9):160-165,110.LI Zheng,LI Yongshu,WU Xi,et al. Hollow village building detection method using high resolution remote sensing image based on CNN[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(9):160-165,110. http:∥www. jcsam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20170920&flag=1&journal_id=jcsam. DOI:10. 6041/j. issn.1000-1298. 2017. 09. 020.(in Chinese)
[19]何小飞,邹峥嵘,陶超,等.联合显著性和多层卷积神经网络的高分影像场景分类[J].测绘学报,2016,45(9):1073-1080.HE Xiaofei,ZOU Zhengrong,TAO Chao,et al. Combined saliency with multi-convolutional neural network for high resolution remote sensing scene classification[J]. Acta Geodaetica et Cartographica Sinica,2016,45(9):1073-1080.(in Chinese)
[20] HE H,QIAN H,XIE L,et al. Applying CNN classifier to road interchange classification[C]∥2018 26th International Conference on Geoinformatics. IEEE,2018.
[21] LONG J,SHELHAMER E,DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence,2014,39(4):640-651.
[22]周云成,许童羽,郑伟,等.基于深度卷积神经网络的番茄主要器官分类识别方法[J].农业工程学报,2017,33(15):219-226.ZHOU Yuncheng,XU Tongyu,ZHENG Wei,et al. Classification and recognition approaches of tomato main organs based on DCNN[J]. Transactions of the CSAE,2017,33(15):219-226.(in Chinese)
[23] PATHAK D,KRAHENBUHL P,DARRELL T. Constrained convolutional neural networks for weakly supervised segmentation[C]∥2015 IEEE International Conference on Computer Vision(ICCV). IEEE Computer Society,2015.
[24] GIRSHICK R,DONAHUE J,DARRELL T,et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). IEEE Computer Society,2014.
[25] CHEN L C,PAPANDREOU G,KOKKIONS I,et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[J]. Computer Science,2014(4):357-361.
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