小麦倒伏信息无人机多时相遥感提取方法
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摘要
采用两期无人机可见光遥感图像,对灌浆期冬小麦倒伏图像特征及倒伏信息提取方法进行研究。从增强图像空间域方面,对图像进行二次低通滤波,获取地物散点图,以散点存在明显分界线为判定标准,选出小麦倒伏信息提取的单特征,对两单特征线性拟合构建倒伏小麦两时期提取特征参数F_1和F_2,再以两特征参数相似性构建综合特征参数F_3。将特征参数结合K-means算法提取冬小麦倒伏信息,整体精度(OA)达86. 44%以上,Kappa系数达0. 73以上,倒伏信息提取精度(F)为81. 07%以上,因此综合特征参数可作为两个时期冬小麦倒伏信息提取特征参数。分别用本文方法、支持向量机、神经网络法和最大似然法提取验证区域倒伏小麦信息,经验证,本文方法提取小麦倒伏信息整体精度(OA)达86. 29%以上,Kappa系数达0. 71以上,倒伏信息提取精度(F)达80. 60%以上;其他3种常用方法提取的整体精度(OA)为69. 68%～87. 44%,Kappa系数为0. 49～0. 72,倒伏信息提取精度(F)为65. 33%～79. 76%。结果表明,本文方法整体精度和倒伏信息提取精度均高于目前常用分类方法。因此,综合特征参数与K-means算法对冬小麦在灌浆期倒伏信息提取具有一定的准确性和适用性。
The information of crop lodging is very important for agricultural hazard assessment and agricultural insurance claims. Remote sensing is a fast and efficient technology to gain the information of crop lodging,but satellite remote sensing cannot provide available data. Recently,unmanned aerial vehicle( UAV) remote sensing system has grown rapidly,and UAV remote sensing system can get available data neatly and fleetly. There was no survey on winter wheat lodging by using multi-temporal UAV remote sensing data. Therefore,a survey method of winter wheat lodging was proposed by using images derived from the UAV remote sensing experiments,which were carried out in the winter wheat test field of Institute of Water-Saving Agriculture in Arid Areas of China( IWSA),Northwest A&F University on May 4 th and 16 th of 2017. Images were handled with the second low pass filter firstly to enhance the image space domain. Then the scatter diagram of lodging and unlodging wheat was gained in different feature combination coordinate systems. The single features of wheat lodging information extraction based on the well-defined boundary of the scatter diagram were selected. Feature parameters F_1 and F_2 were gained by fitting boundary points of May 4 th and 16 th. Using the similarities of F_1 and F_2 can obtain F_3 to extract winter wheat lodging information of two periods. Using F_1,F_2 and F_3 combined with K-means to extract the lodging information of winter wheat. It was turned out that the overall accuracy was over86. 44%,the Kappa coefficient was over 0. 73,and the lodging extracting accuracy was over 81. 07%,so F_3 can be the feature parameter to extract the lodging information of two periods. To research the accuracy and versatility of this method,two verification areas were selected and the method of this paper,support vector machine( SVM),neural network and maximum likelihood method were respectively used to extract the lodging information of winter wheat. The results showed that the overall accuracy,Kappa coefficient and lodging extracting accuracy of the method were over 86. 29%,0. 71 and 80. 60%,and the overall accuracy,Kappa coefficient and lodging extracting accuracy of the other common methods were69. 68% ～ 87. 44%,0. 49 ～ 0. 72 and 65. 33% ～ 79. 76%,respectively. The results indicated that the overall accuracy,Kappa coefficient and lodging extracting accuracy of this method were all tower over other methods. Therefore, the proposed method was accurate and versatile to extract the lodging information of winter wheat in the watery stage.
The information of crop lodging is very important for agricultural hazard assessment and agricultural insurance claims. Remote sensing is a fast and efficient technology to gain the information of crop lodging,but satellite remote sensing cannot provide available data. Recently,unmanned aerial vehicle( UAV) remote sensing system has grown rapidly,and UAV remote sensing system can get available data neatly and fleetly. There was no survey on winter wheat lodging by using multi-temporal UAV remote sensing data. Therefore,a survey method of winter wheat lodging was proposed by using images derived from the UAV remote sensing experiments,which were carried out in the winter wheat test field of Institute of Water-Saving Agriculture in Arid Areas of China( IWSA),Northwest A&F University on May 4 th and 16 th of 2017. Images were handled with the second low pass filter firstly to enhance the image space domain. Then the scatter diagram of lodging and unlodging wheat was gained in different feature combination coordinate systems. The single features of wheat lodging information extraction based on the well-defined boundary of the scatter diagram were selected. Feature parameters F_1 and F_2 were gained by fitting boundary points of May 4 th and 16 th. Using the similarities of F_1 and F_2 can obtain F_3 to extract winter wheat lodging information of two periods. Using F_1,F_2 and F_3 combined with K-means to extract the lodging information of winter wheat. It was turned out that the overall accuracy was over86. 44%,the Kappa coefficient was over 0. 73,and the lodging extracting accuracy was over 81. 07%,so F_3 can be the feature parameter to extract the lodging information of two periods. To research the accuracy and versatility of this method,two verification areas were selected and the method of this paper,support vector machine( SVM),neural network and maximum likelihood method were respectively used to extract the lodging information of winter wheat. The results showed that the overall accuracy,Kappa coefficient and lodging extracting accuracy of the method were over 86. 29%,0. 71 and 80. 60%,and the overall accuracy,Kappa coefficient and lodging extracting accuracy of the other common methods were69. 68% ～ 87. 44%,0. 49 ～ 0. 72 and 65. 33% ～ 79. 76%,respectively. The results indicated that the overall accuracy,Kappa coefficient and lodging extracting accuracy of this method were all tower over other methods. Therefore, the proposed method was accurate and versatile to extract the lodging information of winter wheat in the watery stage.
引文
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[2]杨浩,杨贵军,顾晓鹤,等.小麦倒伏的雷达极化特征及其遥感监测[J].农业工程学报,2014,30(7):1-8.YANG Hao,YANG Guijun,GU Xiaohe,et al.Radar polarimetric response features and remote sensing monitoring of wheat lodging[J].Transactions of the CSAE,2014,30(7):1-8.(in Chinese)
[3]ISLAM M S,PENG S,VISPERAS R M,et al.Lodging-related morphological traits of hybrid rice in a tropical irrigated ecosystem[J].Field Crops Research,2007,101(2):240-248.
[4]BERRY P M,SPINK J.Predicting yield losses caused by lodging in wheat[J].Field Crops Research,2012,137:19-26.
[5]郭相平,甄博,王振昌.旱涝交替胁迫增强水稻抗倒伏性能[J].农业工程学报,2013,29(12):130-135.GUO Xiangping,ZHEN Bo,WANG Zhenchang.Increasing lodging resistance performance of rice by alternating drought and flooding stress[J].Transactions of the CSAE,2013,29(12):130-135.(in Chinese)
[6]BERRY P M,STERLING M,BAKER C J,et al.A calibrated model of wheat lodging compared with field measurements[J].Agricultural and Forest Meteorology,2003,119(3-4):167-180.
[7]陈仲新,任建强,唐华俊,等.农业遥感研究应用进展与展望[J].遥感学报,2016,20(5):748-767.CHEN Zhongxin,REN Jianqiang,TANG Huajun,et al.Progress and perspectives on agricultural remote sensing research and applications in China[J].Journal of Remote Sensing,2016,20(5):748-767.(in Chinese)
[8]刘良云,王纪华,宋晓宇,等.小麦倒伏的光谱特征及遥感监测[J].遥感学报,2005,9(3):323-327.LIU Liangyun,WANG Jihua,SONG Xiaoyu,et al.The canopy spectral features and remote sensing of wheat lodging[J].Journal of Remote Sensing,2005,9(3):323-327.(in Chinese)
[9]张荣群,王盛安,高万林,等.农作物种植格局对遥感分类精度的影响[J/OL].农业机械学报,2016,47(10):318-324.ZHANG Rongqun,WANG Sheng'an,GAO Wanlin,et al.Effects of crop planting structure on remote sensing classification accuracy[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2016,47(10):318-324.http:∥www.jcsam.org/jcsam/ch/reader/view_abstract.aspx?file_no=20161040&flag=1.DOI:10.6041/j.issn.1000-1298.2016.10.040.(in Chinese)
[10]LIU M W,OZDOGAN M,ZHU X.Crop type classification by simultaneous use of satellite images of different resolutions[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(6):3637-3649.
[11]韩东,杨浩,杨贵军,等.基于Sentinel-1雷达影像的玉米倒伏监测模型[J].农业工程学报,2018,34(3):166-172.HAN Dong,YANG Hao,YANG Guijun,et al.Monitoring model of maize lodging based on Sentinel-1 radar image[J].Transactions of the CSAE,2018,34(3):166-172.(in Chinese)
[12]王立志,顾晓鹤,胡圣武,等.基于多时相HJ-1B CCD影像的玉米倒伏灾情遥感监测[J].中国农业科学,2016,49(21):4120-4129.WANG Lizhi,GU Xiaohe,HU Shengwu,et al.Remote sensing monitoring of maize lodging disaster with multi-temporal HJ-1BCCD image[J].Scientia Agricultura Sinica,2016,49(21):4120-4129.(in Chinese)
[13]王猛,隋学艳,梁守真,等.夏玉米倒伏模拟试验及遥感监测[J].测绘科学,2017,42(8):137-141.WANG Meng,SUI Xueyan,LIANG Shouzhen,et al.Simulation test and remote sensing monitor of summer corn lodging[J].Science of Surveying and Mapping,2017,42(8):137-141.(in Chinese)
[14]ZHANG J,GU X,WANG J,et al.Evaluating maize grain quality by continuous wavelet analysis under normal and lodging circumstances[J].NJAS-Wageningen Journal of Life Sciences,2012,10(1-2):580-585.
[15]韩文霆,李广,苑梦婵,等.基于无人机遥感技术的玉米种植信息提取方法研究[J/OL].农业机械学报,2017,48(1):139-147.HAN Wenting,LI Guang,YUAN Mengchan,et al.Extraction method of maize planting information based on uav remote sensing technology[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(1):139-147.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?file_no=20170118&flag=1.DOI:10.6041/j.issn.1000-1298.2017.01.018.(in Chinese)
[16]王柯,付怡然,彭向阳,等.无人机低空遥感技术进展及典型行业应用综述[J].测绘通报,2017(增刊):79-83.WANG Ke,FU Yiran,PENG Xiangyang,et al.Overview of UAV low altitude remote sensing technology and application in typical industries[J].Bulletin of Surveying and Mapping,2017(Supp.):79-83.(in Chinese)
[17]金伟,葛宏立,杜华强,等.无人机遥感发展与应用概况[J].遥感信息,2009(1):88-92.JIN Wei,GE Hongli,DU Huaqiang,et al.A review on unmanned aerial vehicle remote sensing and its application[J].Remote Sensing Information,2009(1):88-92.(in Chinese)
[18]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 Deren,LI Ming.Research advance and application prospect of unmanned aerial vehicle remote sensing system[J].Geomatics and Information Science of Wuhan University,2014,39(5):505-513.(in Chinese)
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