无人机遥感影像面向对象分类方法估算市域水稻面积
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摘要
针对如何高效地从无人机遥感影像中提取农作物样方数据,用于农作物面积遥感估算,该文以浙江省平湖市为例,利用面向对象分类方法对无人机影像进行水稻自动化识别,作为样方数据与卫星遥感全覆盖空间分布分类结果结合,采用分层联合比估计进行2014年单季晚稻面积估算。然后,与人工目视解译识别方法获取的水稻样方数据推断的区域水稻面积估算的结果进行精度、效率对比分析。研究结果表明:1)利用面向对象分类方法对无人机影像进行分类,总体分类精度达到93%以上,满足构建样本的要求;2)通过区域作物估算对比分析发现,面向对象分类方法对无人机影像进行水稻识别,构建平湖市单季晚稻的样方数据,能够替代人工目视解译样方准确推断区域作物种植面积,有效地提高了无人机影像在遥感面积估算中的应用效率。
Unmanned aerial vehicle(UAV) is a highly efficient technology to detect land surface, which is capable of acquiring centimeter-level spatial resolution data and acquiring ground survey samples information timely and accurately. This is the basis for large-scale crop acreage estimation. However, the usual procedure of UAV image for sampling plot identification is still hand-generated, which leads to time-consuming and high cost expense. Therefore, it is urgent to develop an efficient approach to extract crop acreage in sampling plot from UAV orthographic image to support the regional crop acreage estimation. In this paper, we introduced an object-oriented automatic classification method, instead of the visually digitized method, to identify UAV quadrat paddy, and the combination of the quadrat paddy data and the paddy classification result from satellite remote sensing was exploited to estimate the paddy planting acreage. The comprehensive comparison between the methods of manual visual interpretation and object-oriented classification was carried out. This experiment was conducted in Pinghu City, Zhejiang Province. The integrated satellite imageries of Chinese GF-1 WFV sensor and American Landsat8 OLI were acquired. According to paddy phenology calendar of the study area, there were 2 key phases for the paddy identification. In middle June, paddy was in the seedling growing season, which represented the water spectrum. However, in later July, the paddy rice field showed the vegetation spectral information in satellite imagery, which was at tillering stage. The information sources of GF-1 WFV and Landsat8 OLI acquired at the respective phenological stages were sufficient to map paddy rice distribution. First, the data were preprocessed for geometric correction, and atmospheric correction was applied for both satellite images. Support vector machine(SVM) was used to identify the vegetation and water components from GF-1 data and Landsat8 data, respectively. Then, a logical "and" operation was conducted between vegetation and water to generate the paddy rice spatial distribution. UAV images were obtained from T10 Bumblebee platform, and a total of 7 sampling belts were acquired. Amount of UAV photos was firstly tiled by Pix4 D mapper to generate UAV images with the resolution of 0.08 m. Then, UAV images were segmented by multi-scale algorithm in e Cognition Developer, the segmentation scale was set to 50, 100, 150, 200 and 250 and the spectral standard deviation of image objects and its rate were calculated at each scale to determine the optimal segmentation scale. After various tests, the choice of 200 was decided as the optimal segmentation scale to describe the field boundary clearly and correctly. Then, different features were constructed and nearest neighbor classification method was adopted to extract the paddy planting distribution in sampling belts. A framework of 300 m × 300 m square grids was built covering the extent of paddy as the primary sample unit, and then the sampling ratios were calculated using acreage index to allocate the samples in each stratum. Then, the regional paddy rice acreage was estimated by combining extracted paddy rice acreage based on the satellite remote sensing and UAV sampling belt. The results showed that the overall classification accuracy of paddy from UAV image was more than 93% for the object-oriented automatic classification method, which met the basic requirements of building the samples. Most of all, the difference of CVs(coefficient of variations) of the acreage estimation aided by automatic classification quadrat data and manual visual interpretation quadrat data was 0.0008, which stated the object-oriented automatic classification could achieve the same estimation performance as artificial visual interpretation method. That implies it is feasible to apply the object-oriented automatic classification in place of the visual digitization method to extract the quadrat data to support the regional paddy acreage estimation. This achievement can be applied and tested extensively in large-scale areas and with different crops.
Unmanned aerial vehicle(UAV) is a highly efficient technology to detect land surface, which is capable of acquiring centimeter-level spatial resolution data and acquiring ground survey samples information timely and accurately. This is the basis for large-scale crop acreage estimation. However, the usual procedure of UAV image for sampling plot identification is still hand-generated, which leads to time-consuming and high cost expense. Therefore, it is urgent to develop an efficient approach to extract crop acreage in sampling plot from UAV orthographic image to support the regional crop acreage estimation. In this paper, we introduced an object-oriented automatic classification method, instead of the visually digitized method, to identify UAV quadrat paddy, and the combination of the quadrat paddy data and the paddy classification result from satellite remote sensing was exploited to estimate the paddy planting acreage. The comprehensive comparison between the methods of manual visual interpretation and object-oriented classification was carried out. This experiment was conducted in Pinghu City, Zhejiang Province. The integrated satellite imageries of Chinese GF-1 WFV sensor and American Landsat8 OLI were acquired. According to paddy phenology calendar of the study area, there were 2 key phases for the paddy identification. In middle June, paddy was in the seedling growing season, which represented the water spectrum. However, in later July, the paddy rice field showed the vegetation spectral information in satellite imagery, which was at tillering stage. The information sources of GF-1 WFV and Landsat8 OLI acquired at the respective phenological stages were sufficient to map paddy rice distribution. First, the data were preprocessed for geometric correction, and atmospheric correction was applied for both satellite images. Support vector machine(SVM) was used to identify the vegetation and water components from GF-1 data and Landsat8 data, respectively. Then, a logical "and" operation was conducted between vegetation and water to generate the paddy rice spatial distribution. UAV images were obtained from T10 Bumblebee platform, and a total of 7 sampling belts were acquired. Amount of UAV photos was firstly tiled by Pix4 D mapper to generate UAV images with the resolution of 0.08 m. Then, UAV images were segmented by multi-scale algorithm in e Cognition Developer, the segmentation scale was set to 50, 100, 150, 200 and 250 and the spectral standard deviation of image objects and its rate were calculated at each scale to determine the optimal segmentation scale. After various tests, the choice of 200 was decided as the optimal segmentation scale to describe the field boundary clearly and correctly. Then, different features were constructed and nearest neighbor classification method was adopted to extract the paddy planting distribution in sampling belts. A framework of 300 m × 300 m square grids was built covering the extent of paddy as the primary sample unit, and then the sampling ratios were calculated using acreage index to allocate the samples in each stratum. Then, the regional paddy rice acreage was estimated by combining extracted paddy rice acreage based on the satellite remote sensing and UAV sampling belt. The results showed that the overall classification accuracy of paddy from UAV image was more than 93% for the object-oriented automatic classification method, which met the basic requirements of building the samples. Most of all, the difference of CVs(coefficient of variations) of the acreage estimation aided by automatic classification quadrat data and manual visual interpretation quadrat data was 0.0008, which stated the object-oriented automatic classification could achieve the same estimation performance as artificial visual interpretation method. That implies it is feasible to apply the object-oriented automatic classification in place of the visual digitization method to extract the quadrat data to support the regional paddy acreage estimation. This achievement can be applied and tested extensively in large-scale areas and with different crops.
引文
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[2]陈水森,柳钦火,陈良富,等.粮食作物播种面积遥感监测研究进展[J].农业工程学报,2005,21(6):166-171.Chen Shuisen,Liu Qinhuo,Chen Liangfu,et al.Review of research advances in remote sensing monitoring of grain crop area[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2005,21(6):166-171.(in Chinese with English abstract)
[3]魏新彩.基于HJ卫星影像的水稻种植面积遥感信息提取方法研究[D].武汉:湖北大学,2013.Wei Xincai.Extraction of Paddy Rice Planting Area Based on Environmental Satellite Image[D].Wuhan:Hubei University,2013.(in Chinese with English abstract)
[4]钱永兰,杨邦杰,焦险峰.基于遥感抽样的国家尺度农作物面积统计方法评估[J].农业工程学报,2007,23(11):180-187.Qian Yonglan,Yang Bangjie,Jiao Xianfeng.Accuracy assessment on the crop area estimating method based on RSsampling at national scale[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2007,23(11):180-187.(in Chinese with English abstract)
[5]孙佩军,张锦水,潘耀忠,等.基于无人机样方事后分层的作物面积估算[J].中国农业资源与区划,2016,37(2):1-10.Sun Peijun,Zhang Jinshui,Pan Yaozhong,et al.Study on the post-stratification method for crop area estimation based on unmanned aerial vehicle images[J].Chinese Journal of Agricultural Resources and Regional Planning,2016,37(2):1-10.(in Chinese with English abstract)
[6]申克建,何浩,蒙红卫,等.农作物面积空间抽样调查研究进展[J].中国农业资源与区划,2012,33(4):11-16.Shen Kejian,He Hao,Meng Hongwei,et al.Review on spatial sampling survey in crop area estimation[J].Chinese Journal of Agricultural Resources and Regional Planning,2012,33(4):11-16.(in Chinese with English abstract)
[7]Tsiligirides T A.Remote sensing as a tool for agricultural statistics:A case study of area frame sampling methodology in Hellas[J].Computers&Electronics in Agriculture,1998,20(1):45-77.
[8]许文波,田亦陈.作物种植面积遥感提取方法的研究进展[J].云南农业大学学报,2005,20(1):94-98.Xu Wenbo,Tian Yichen.Overview of extraction of crop area from remote sensing[J].Journal of Yunnan Agricultural University,2005,20(1):94-98.(in Chinese with English abstract)
[9]赵庚星,余松烈.冬小麦遥感估产研究进展[J].山东农业大学学报自然科学版,2001,32(1):107-111.Zhao Gengxing,Yu Songlie.Advance of winter wheat yield estimation by remote sensing[J].Journal of Shandong Agricultural University:Natural Science,2001,32(1):107-111.(in Chinese with English abstract)
[10]刘海启.欧盟MARS计划简介与我国农业遥感应用思路[J].中国农业资源与区划,1999,20(3):55-57.Liu Haiqi.The introduction of mars plan of European commission and Chinese agriculture remote sensing application[J].Chinese Journal of Agricultural Resources and Regional Planning,1999,20(3):55-57.(in Chinese with English abstract)
[11]吴炳方,蒙继华,李强子.国外农情遥感监测系统现状与启示[J].地球科学进展,2010,25(10):1003-1012.Wu Bingfang,Meng Jihua,Li Qiangzi.Review of overseas crop monitoring system with remote sensing[J].Advances in Earth Science,2010,25(10):1003-1012.(in Chinese with English abstract)
[12]吴炳方.中国农情遥感速报系统[J].遥感学报,2004,8(6):481-497.Wu Bingfang.China crop watch system with remote sensing[J].Journal of Remote Sensing,2004,8(6):481-497.(in Chinese with English abstract)
[13]施潮.关于大面积农作物产量抽样实测的做法[J].湖北农业科学学报,1965(2):55-61.Shi Chao.The practice of sampling large area crop yields[J].Journal of Hubei Agricultural Science,1965(2):55-61.(in Chinese with English abstract)
[14]Keita Naman,Statistician Senior,Ammar George Mu.Issues and guidelines for the emerging use of GPS and PDAs in agricultural statistics in developing countries[C]//The Fifth International Conference on Agricultural Statistics(ICAS V),Kampala,Uganda.2010.
[15]李恩宝,洪洲,宋伟东.基于PDA的土地利用变更调查系统设计与实现[J].测绘工程,2007,16(2):61-64.Li Enbao,Hong Zhou,Song Weidong.Design and realization of land use alteration investigation system based on PDA[J].Engineering of Surveying and Mapping,2007,16(2):61-64.(in Chinese with English abstract)
[16]王利民,刘佳,杨玲波,等.基于无人机影像的农情遥感监测应用[J].农业工程学报,2013,29(18):136-145.Wang Limin,Liu Jia,Yang Lingbo,et al.Applications of unmanned aerial vehicle images on agricultural remote sensing monitoring[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(18):136-145.(in Chinese with English abstract)
[17]Herwitz S R,Johnson L F,Dunagan S E,et al.Imaging from an unmanned aerial vehicle:Agricultural surveillance and decision support[J].Computers&Electronics in Agriculture,2004,44(1):49-61.
[18]Lelong C C D,Burger P,Jubelin G,et al.Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots[J].Sensors,2008,8(5):3557-3585.
[19]Senthilnath J,Dokania A,Kandukuri M,et al.Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV[J].Biosystems Engineering,2016(146):16-32.
[20]Shen Kejian,Li Weifang,Pei Zhiyuan,et al.Crop area estimation from UAV transect and MSR image data using spatial sampling method[J].Procedia Environmental Sciences,2015(26):95-100.
[21]Breckenridge R P.Improving rangeland monitoring and assessment:integrating remote sensing,GIS,and unmanned aerial vehicle systems[J].Dissertation Abstracts International,2007,68(6):3655.
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