无人机影像的面向对象水稻种植面积快速提取
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摘要
基于抽样技术的地面调查与遥感影像分类相结合的方法在大范围作物种植面积提取中得到广泛使用。无人机影像具有低成本、高时效、高分辨率的一系列优点,可以快速实现特定区域范围内的农情采样任务。本文以水稻样地为研究对象,采用便携式无人机Mavic Pro进行航拍。对所获取无人机影像进行预处理生成分辨率为3.95cm/pix的正射影像,采用面向对象的思想,目视评价和ESP工具相结合快速选择了最优分割尺度为300,应用了支持向量机、随机森林和最邻近监督分类方法对影像进行了地物分类和水稻面积快速提取。采用目视解译分类结果进行分类结果和面积精度评价,总体精度最高的方法为最邻近分类法,此时水稻分类用户精度为95%,面积一致性精度为99%。研究结果说明了无人机遥感和自动分类能够在平原水稻种植区快速获取样方内高分辨率影像并提取水稻种植面积,弥补了农田被遮挡时地面调查数据的缺失,为大范围水稻种植面积、产量等信息的计算提供样本和验证依据。
The methodology of combining sampling-based ground survey and satellite imagery classification has been widely used in estimating crop acreage on large scales. Use of unmanned aerial vehicle(UAV) imagery has a series of merits including low cost, high efficiency, and high resolution, which make it possible to quickly monitor the agricultural conditions over a specific area. With a research focus on rice sample plots, this study used a portable UAV Mavic Pro to obtain aerial imagery. The UAV imagery were preprocessed to generate an orthophoto with a resolution of 3.95 cm/pix. By adopting the object-oriented classification philosophy, visual assessment, and the Estimation of Scale Parameter(ESP) tool, the optimal segmentation scale was determined to be 300. The support vector machine, random forest, and nearest neighborhood classifiers were employed and contrasted for imagery classification and the extraction of rice acreage; visual interpretation was used for assessing the accuracy of the classification results. The best automatic classification method turned out to be nearest neighborhood classification, with its user accuracy of rice being 95% and the area consistency accuracy99%. The findings show that use of UAV imagery and automatic classification can quickly acquire highresolution imagery and extract rice acreage in rice growing areas on plains. Moreover, high-resolution UAV imagery can be used as ground truth data when cropland is in shadow. The proposed approach helps provide validation samples for estimating rice acreage and production on large scales.
The methodology of combining sampling-based ground survey and satellite imagery classification has been widely used in estimating crop acreage on large scales. Use of unmanned aerial vehicle(UAV) imagery has a series of merits including low cost, high efficiency, and high resolution, which make it possible to quickly monitor the agricultural conditions over a specific area. With a research focus on rice sample plots, this study used a portable UAV Mavic Pro to obtain aerial imagery. The UAV imagery were preprocessed to generate an orthophoto with a resolution of 3.95 cm/pix. By adopting the object-oriented classification philosophy, visual assessment, and the Estimation of Scale Parameter(ESP) tool, the optimal segmentation scale was determined to be 300. The support vector machine, random forest, and nearest neighborhood classifiers were employed and contrasted for imagery classification and the extraction of rice acreage; visual interpretation was used for assessing the accuracy of the classification results. The best automatic classification method turned out to be nearest neighborhood classification, with its user accuracy of rice being 95% and the area consistency accuracy99%. The findings show that use of UAV imagery and automatic classification can quickly acquire highresolution imagery and extract rice acreage in rice growing areas on plains. Moreover, high-resolution UAV imagery can be used as ground truth data when cropland is in shadow. The proposed approach helps provide validation samples for estimating rice acreage and production on large scales.
引文
[1] Wu B F, Rene G, Zhang M, et al. Global crop monitoring:A satellite-based hierarchical approach[J]. Remote Sensing, 2015,7(4):3907-3933.
[2] Wu B, Li Q. Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes[J]. International Journal of Applied Earth Observation and Geoinformation, 2012,16:101-112.
[3]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 W Bo,Tian Y C. Overview of extraction of crop area from remote sensing[J]. Journal of Yunnan Agricultural University, 2005,20(1):94-98.]
[4]张淼.农田利用状况动态变化识别方法与应用研究[D].北京:中国科学院大学,2014.[Zhang M. Study on the dynamic monitoring of arable land utilization and its applications[D]. Beijing:University of Chinese Academy of Sciences, 2014.]
[5]唐晓风,王乃斌,杨小唤.粮食种植面积提取方法的发展与现状[J].资源科学,2002,24(5):8-12.[Kang X F, Wang N B, Yang X H. Progress of information extraction methods for crop planting area[J]. Resources Science,2002,24(5):8-12.]
[6]吴炳方,李强子.基于两个独立抽样框架的农作物种植面积遥感估算方法[J].遥感学报,2004,8(6):551-569.[Wu B F, Li Q Z. Crop acreage estimation using two individual sampling frameworks with stratification[J]. Journal of Remote Sensing, 2004,8(6):551-569.]
[7]吴炳方,田亦陈,李强子. GVG农情采样系统及其应用[J].遥感学报,2004,8(6):570-580.[Wu B F, Tian Y C, Li Q Z. GVG, a crop type proportion sampling instrument[J]. Journal of Remote Sensing, 2004,8(6):570-580.]
[8]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,2014,5(39):505-513.[Li D R, Li M. Research advance and application prospect of unmanned aerial vehicle remote sensing system[J]. Geomatics and Information Science of Wuhan University,2014,5(39):505-513.]
[9]陈天博,胡卓玮,魏铼,等.无人机遥感数据处理与滑坡信息提取[J].地球信息科学学报,2017,19(5):692-701.[Chen T B, Hu Z W, Wei L, et al. Data processing and landslide information extraction based on UAV remote sensing[J]. Journal of Geo-information Science, 2017,19(5):692-701.]
[10] Bruce E, David A. 2015 Precision agricultural services dealership survey results[R]. Indiana:Purdue University,2015.
[11]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,2013,18(29):136-146.[Wang L M, Liu J, Yang L B, et al. Applications of unmanned aerial vehicle images on agricultural remote sensing monitoring[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013,29(18):136-145.]
[12]闵文芳,江朝晖,李想,等.基于无人机平台和图像分析的田间作物检测[J].湖南农业大学学报,2017,43(2):212-216.[Min W F, Jiang Z H, LI X, et al. Area measurement for field crops based on UAV platform and image analysis[J]. Journal of Hunan Agricultural University(Natural Sciences), 2017,43(2):212-216.]
[13] Seungtaek J, Jonghan K, Mijeong K, et al. Construction of an unmanned aerial vehicle remote sensing system for crop monitoring[J]. Journal of Applied Remote Sensing,2016,10(2):1-14.
[14] Zhou X, Zheng H B, Xu X Q, et al. Predicting grain yield in rice using multi-temporal vegetation indices from UAVbased multispectral and digital imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017,130:246-255.
[15]吴金胜,刘红利,张锦水.无人机遥感影像面向对象分类方法估算市域水稻面积[J].农业工程学报,2018,34(1):70-77.[Wu J S, Liu H L, Zhang J S. Paddy planting acreage estimation in city level based on UAV images and object-oriented classification method[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(1):70-77.]
[16]王慧.PhotoScan在无人机遥感影像数据处理中的应用[J].测绘与空间地理信息,2017,40(5):109-111.[Wang H.Application of PhotoScan in UAV remote sensing image data processing[J]. Geomatics and Spatial Information Technology, 2017,40(5):109-111.]
[17]王峰,吴云东.无人机遥感平台技术研究与应用[J].遥感信息,2010(2):114-118.[Wang F, Wu Y D. Research and application of UAS borne remote sensing[J]. Remote Sensing Information, 2010(2):114-118.]
[18]曹宝,秦其明,马海建,等.面向对象方法在SPOT5遥感图像分类中的应用-以北京市海淀区为例[J].地理与地理信息科学,2006,22(2):46-49.[Cao B, Qin Q M, Ma H J,et al. Application of object-oriented approach to SPOT5image classification:A case study in Haidian District, Beijing City[J]. Geography and GEO-Information Science,2006,22(2):46-49.]
[19]何少林,徐京华,张帅毅.面向对象的多尺度无人机影像土地利用信息提取[J].国土资源遥感,2013,25(2):107-112.[He S L, Xu J H, Zhang S Y. Land use classification of object-oriented multi-scale by UAV image[J]. Remote Sensing For Land and Resources, 2013,25(20):107-112.]
[20]李秦,高锡章,张涛,等.最优分割尺度下的多层次遥感地物分类实验分析[J].地球信息科学学报,2011,13(3):409-417.[Li Q, Gao X Z, Zhang T, et al. Optimal segmentation scale selection and evaluation for multi-layer image recognition and classification[J]. Journal of Geo-information science, 2011,13(3):409-417.]
[21] Lucian D, Dirk T Shaun R. ESP:A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data[J]. International Journal of Geographical Information Science, 2010,24(6):859-871.
[22]孙家波.基于知识的高分辨率遥感影像耕地自动提取技术研究[D].北京:中国农业大学,2014.[Sun J B. Study on automatic cultivated land extraction from high resolution satellite imagery based on konwledge[D]. Beijing:China Agricultural University, 2014.]
[23]全国绿化委员会办公室. 2017年中国国土绿化状况公报[N].人民日报,2018-03-13(017).[National Aflorestation Environmental Protection Commission. Bulletins on aflorestation environmental situations of China in 2017[N]. People's Daily, 2018-03-13(017).]
[24]李明,黄愉淇,李绪孟,等.基于无人机遥感影像的水稻种植信息提取[J].农业工程学报,2018,34(4):108-114.[Li M, Huang Y Q, Li X M, et al. Extraction of rice planting information based on remote sensing image from UAV[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(4):108-114.]
[2] Wu B, Li Q. Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes[J]. International Journal of Applied Earth Observation and Geoinformation, 2012,16:101-112.
[3]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 W Bo,Tian Y C. Overview of extraction of crop area from remote sensing[J]. Journal of Yunnan Agricultural University, 2005,20(1):94-98.]
[4]张淼.农田利用状况动态变化识别方法与应用研究[D].北京:中国科学院大学,2014.[Zhang M. Study on the dynamic monitoring of arable land utilization and its applications[D]. Beijing:University of Chinese Academy of Sciences, 2014.]
[5]唐晓风,王乃斌,杨小唤.粮食种植面积提取方法的发展与现状[J].资源科学,2002,24(5):8-12.[Kang X F, Wang N B, Yang X H. Progress of information extraction methods for crop planting area[J]. Resources Science,2002,24(5):8-12.]
[6]吴炳方,李强子.基于两个独立抽样框架的农作物种植面积遥感估算方法[J].遥感学报,2004,8(6):551-569.[Wu B F, Li Q Z. Crop acreage estimation using two individual sampling frameworks with stratification[J]. Journal of Remote Sensing, 2004,8(6):551-569.]
[7]吴炳方,田亦陈,李强子. GVG农情采样系统及其应用[J].遥感学报,2004,8(6):570-580.[Wu B F, Tian Y C, Li Q Z. GVG, a crop type proportion sampling instrument[J]. Journal of Remote Sensing, 2004,8(6):570-580.]
[8]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,2014,5(39):505-513.[Li D R, Li M. Research advance and application prospect of unmanned aerial vehicle remote sensing system[J]. Geomatics and Information Science of Wuhan University,2014,5(39):505-513.]
[9]陈天博,胡卓玮,魏铼,等.无人机遥感数据处理与滑坡信息提取[J].地球信息科学学报,2017,19(5):692-701.[Chen T B, Hu Z W, Wei L, et al. Data processing and landslide information extraction based on UAV remote sensing[J]. Journal of Geo-information Science, 2017,19(5):692-701.]
[10] Bruce E, David A. 2015 Precision agricultural services dealership survey results[R]. Indiana:Purdue University,2015.
[11]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,2013,18(29):136-146.[Wang L M, Liu J, Yang L B, et al. Applications of unmanned aerial vehicle images on agricultural remote sensing monitoring[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013,29(18):136-145.]
[12]闵文芳,江朝晖,李想,等.基于无人机平台和图像分析的田间作物检测[J].湖南农业大学学报,2017,43(2):212-216.[Min W F, Jiang Z H, LI X, et al. Area measurement for field crops based on UAV platform and image analysis[J]. Journal of Hunan Agricultural University(Natural Sciences), 2017,43(2):212-216.]
[13] Seungtaek J, Jonghan K, Mijeong K, et al. Construction of an unmanned aerial vehicle remote sensing system for crop monitoring[J]. Journal of Applied Remote Sensing,2016,10(2):1-14.
[14] Zhou X, Zheng H B, Xu X Q, et al. Predicting grain yield in rice using multi-temporal vegetation indices from UAVbased multispectral and digital imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017,130:246-255.
[15]吴金胜,刘红利,张锦水.无人机遥感影像面向对象分类方法估算市域水稻面积[J].农业工程学报,2018,34(1):70-77.[Wu J S, Liu H L, Zhang J S. Paddy planting acreage estimation in city level based on UAV images and object-oriented classification method[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(1):70-77.]
[16]王慧.PhotoScan在无人机遥感影像数据处理中的应用[J].测绘与空间地理信息,2017,40(5):109-111.[Wang H.Application of PhotoScan in UAV remote sensing image data processing[J]. Geomatics and Spatial Information Technology, 2017,40(5):109-111.]
[17]王峰,吴云东.无人机遥感平台技术研究与应用[J].遥感信息,2010(2):114-118.[Wang F, Wu Y D. Research and application of UAS borne remote sensing[J]. Remote Sensing Information, 2010(2):114-118.]
[18]曹宝,秦其明,马海建,等.面向对象方法在SPOT5遥感图像分类中的应用-以北京市海淀区为例[J].地理与地理信息科学,2006,22(2):46-49.[Cao B, Qin Q M, Ma H J,et al. Application of object-oriented approach to SPOT5image classification:A case study in Haidian District, Beijing City[J]. Geography and GEO-Information Science,2006,22(2):46-49.]
[19]何少林,徐京华,张帅毅.面向对象的多尺度无人机影像土地利用信息提取[J].国土资源遥感,2013,25(2):107-112.[He S L, Xu J H, Zhang S Y. Land use classification of object-oriented multi-scale by UAV image[J]. Remote Sensing For Land and Resources, 2013,25(20):107-112.]
[20]李秦,高锡章,张涛,等.最优分割尺度下的多层次遥感地物分类实验分析[J].地球信息科学学报,2011,13(3):409-417.[Li Q, Gao X Z, Zhang T, et al. Optimal segmentation scale selection and evaluation for multi-layer image recognition and classification[J]. Journal of Geo-information science, 2011,13(3):409-417.]
[21] Lucian D, Dirk T Shaun R. ESP:A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data[J]. International Journal of Geographical Information Science, 2010,24(6):859-871.
[22]孙家波.基于知识的高分辨率遥感影像耕地自动提取技术研究[D].北京:中国农业大学,2014.[Sun J B. Study on automatic cultivated land extraction from high resolution satellite imagery based on konwledge[D]. Beijing:China Agricultural University, 2014.]
[23]全国绿化委员会办公室. 2017年中国国土绿化状况公报[N].人民日报,2018-03-13(017).[National Aflorestation Environmental Protection Commission. Bulletins on aflorestation environmental situations of China in 2017[N]. People's Daily, 2018-03-13(017).]
[24]李明,黄愉淇,李绪孟,等.基于无人机遥感影像的水稻种植信息提取[J].农业工程学报,2018,34(4):108-114.[Li M, Huang Y Q, Li X M, et al. Extraction of rice planting information based on remote sensing image from UAV[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(4):108-114.]