基于无人机遥感影像监测土地整治项目道路沟渠利用情况
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摘要
为客观监测和有效评价土地整治项目基础设施建后利用情况,初步探讨利用无人机航拍影像结合智能算法识别设施利用状态的可能性,该文选取典型项目,利用多旋翼无人机航拍获取高分辨率影像,提取田间道路和骨干沟渠影像网格切片,通过BoW模型构建典型地物样本特征库基于样本纹理特征进行分类,利用支持向量机模型对研究区骨干线状基础设施利用状况进行识别,并依据目视解译和实地勘察对识别结果进行了精度验证。结果显示无人机遥感方法可以初步识别研究区基础设施建后利用情况;研究区田间道路病害和骨干沟渠淤塞情况识别总体分类精度达到80%和70%;田间道路分类误差主要来自通行不畅与路面裂缝,骨干沟渠分类误差主要来自轻度淤塞;提高影像精度情况下,田间道路利用状况识别精度有所提升但不显著,骨干沟渠通畅状况识别精度无明显变化,模型对宽度2 m以下沟渠识别结果精度较差。研究表明,基于无人机遥感对土地整治项目基础设施利用情况进行自动分类识别具有可行性且效率较高,而监测精度有待于后期进一步提升。
The infrastructure in land consolidation projects provide important guarantee for harvest and natural calamities resistance to farmers directly, it is fundamental for rural social and economic development. However, some facilities in land consolidation projects cause problems such as fractured pavement or canal silted up after construction, which can bring negative effects to agricultural production. So it is important to find an effective and reliable technical method to monitor and evaluate the effects of land consolidation projects. Unmanned aerial vehicle(UAV) remote sensing is widely used in feature recognition, roads and canals collection and crop productivity evaluation during recent years, but it is rarely used to evaluate the quality of the infrastructure of land consolidation. To objectively monitor and effectively evaluate the post-construction utilization of the infrastructure in land consolidation projects, this paper selected typical land consolidation projects, used the multi-rotor UAV for aerial photography test to obtain high-resolution aerial images, and put forward the complete technical method and operational procedures for monitoring and evaluation of land consolidation infrastructure. Route 1 covered the whole study area. Route 2 a mainly took pictures on main field roads and canals for precision shooting, and route 2 b focused on roads and canals due to their width were less than 2 m. After image processing, this paper gained image grids of field roads and canals which were wider than 2 m, then selected BoW(bag of words) model to build a sample feature database of surface features, including the pavement diseases and canal silted up such as fractured pavement, obstructed pavement, potholed pavement, canal mild silted up and canal severe silted up. The BoW model included speeded-up robust features(SURF) algorithm for image characteristic representation, and image visual dictionary for local feature clustering. Finally this paper used SVM(support vector machine) to classify the images. The results showed that: UAV remote sensing could monitor and locate the condition of infrastructure post-construction utilization under sunny and cloudless days. Using the method introduced in this paper and combined with the visual interpretation and field survey, the total accuracy rate of field roads reached 80%, and the total classification accuracy rate of canals was about 70%. The cross accuracy rate of field roads and canals was about 70%. The main problem of infrastructure post-construction utilization in the study area was the road obstruction and mild silted up of the canals caused by delayed management and maintenance. After monitoring, this paper analyzed the causes of the differences of monitoring ratio between field roads and canals, and especially explained the causes of the lower monitoring ratio of canals in details. They were as follows: first, the training samples may not match the actual objects in the maps, which caused the extracted information of blocked canals incomplete; second, the spectral information of vegetation and canal water shared the same characteristic in the visible-band image, which might interference the model. This paper also used higher resolution image and linear infrastructure under 2 m to validate the reliability of the model. Route 2 a was used to validate the classification accuracy due to the image resolution was higher. Route 2 b was used to validate the classification accuracy due to the linear infrastructure width was under 2 m. We found that the overall accuracy of linear infrastructure increased insignificantly while the image resolution higher, meanwhile the overall accuracy of linear infrastructure decreased remarkably when the road and canal width was less than 2 m. During the process of UAV remote sensing for monitoring linear infrastructure post-construction utilization such as field roads and canals of land consolidation projects, we can use the high-resolution image efficiently in sunny and cloudless condition, and at the same time there is still much room for improvement.
The infrastructure in land consolidation projects provide important guarantee for harvest and natural calamities resistance to farmers directly, it is fundamental for rural social and economic development. However, some facilities in land consolidation projects cause problems such as fractured pavement or canal silted up after construction, which can bring negative effects to agricultural production. So it is important to find an effective and reliable technical method to monitor and evaluate the effects of land consolidation projects. Unmanned aerial vehicle(UAV) remote sensing is widely used in feature recognition, roads and canals collection and crop productivity evaluation during recent years, but it is rarely used to evaluate the quality of the infrastructure of land consolidation. To objectively monitor and effectively evaluate the post-construction utilization of the infrastructure in land consolidation projects, this paper selected typical land consolidation projects, used the multi-rotor UAV for aerial photography test to obtain high-resolution aerial images, and put forward the complete technical method and operational procedures for monitoring and evaluation of land consolidation infrastructure. Route 1 covered the whole study area. Route 2 a mainly took pictures on main field roads and canals for precision shooting, and route 2 b focused on roads and canals due to their width were less than 2 m. After image processing, this paper gained image grids of field roads and canals which were wider than 2 m, then selected BoW(bag of words) model to build a sample feature database of surface features, including the pavement diseases and canal silted up such as fractured pavement, obstructed pavement, potholed pavement, canal mild silted up and canal severe silted up. The BoW model included speeded-up robust features(SURF) algorithm for image characteristic representation, and image visual dictionary for local feature clustering. Finally this paper used SVM(support vector machine) to classify the images. The results showed that: UAV remote sensing could monitor and locate the condition of infrastructure post-construction utilization under sunny and cloudless days. Using the method introduced in this paper and combined with the visual interpretation and field survey, the total accuracy rate of field roads reached 80%, and the total classification accuracy rate of canals was about 70%. The cross accuracy rate of field roads and canals was about 70%. The main problem of infrastructure post-construction utilization in the study area was the road obstruction and mild silted up of the canals caused by delayed management and maintenance. After monitoring, this paper analyzed the causes of the differences of monitoring ratio between field roads and canals, and especially explained the causes of the lower monitoring ratio of canals in details. They were as follows: first, the training samples may not match the actual objects in the maps, which caused the extracted information of blocked canals incomplete; second, the spectral information of vegetation and canal water shared the same characteristic in the visible-band image, which might interference the model. This paper also used higher resolution image and linear infrastructure under 2 m to validate the reliability of the model. Route 2 a was used to validate the classification accuracy due to the image resolution was higher. Route 2 b was used to validate the classification accuracy due to the linear infrastructure width was under 2 m. We found that the overall accuracy of linear infrastructure increased insignificantly while the image resolution higher, meanwhile the overall accuracy of linear infrastructure decreased remarkably when the road and canal width was less than 2 m. During the process of UAV remote sensing for monitoring linear infrastructure post-construction utilization such as field roads and canals of land consolidation projects, we can use the high-resolution image efficiently in sunny and cloudless condition, and at the same time there is still much room for improvement.
引文
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[3]王玉莹,金晓斌,范业婷,等.农村土地整治对促进农业现代化水平的影响分析[J].中国土地科学,2017,31(8):69-76.Wang Yuying,Jin Xiaobin,Fan Yeting,et al.The impact of rural land consolidation program on promoting the level of agricultural modernization[J].China Land Science,2017,31(8):69-76.(in Chinese with English abstract)
[4]杨绪红,金晓斌,郭贝贝,等.2006-2012年中国土地整治项目投资时空分析[J].农业工程学报,2014,30(8):227-235,294.Yang Xuhong,Jin Xiaobin,Guo Beibei,et al.Spatiotemporal differentiation of land consolidation investment in China form 2006 to 2012[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(8):227-235,294.(in Chinese with English abstract)
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[6]张兵,崔系民,赵彦博,等.基于High-1卫星影像的土地整治遥感监测方法研究与实践[J].农业工程学报,2015,31(20):225-233.Zhang Bing,Cui Ximin,Zhao Yanbo,et al.Method and application of remote sensing monitoring in land consolidation based on High-1 satellite image[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(20):225-233.(in Chinese with English abstract)
[7]罗开盛,李仁东,常变蓉,等.面向对象的湖北省土地覆被变化遥感快速监测[J].农业工程学报,2013,29(24):260-267.Luo Kaisheng,Li Rendong,Chang Bianrong,et al.Dynamic monitoring land-cover change in Hubei province using object-oriented technology and remote sensing imagery[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(24):260-267.(in Chinese with English abstract)
[8]徐秀云,陈向,刘宝梅.微型无人机助力土地整治项目监管[J].测绘通报,2017(3):86-90.Xu Xiuyun,Chen Xiang,Liu Baomei.Using micro-UAVs for the land consolidation and rehabilitation projects supervision[J].Bulletin of Surveying and Mapping,2017(3):86-90.(in Chinese with English abstract)
[9]孟宪素,李少帅.遥感技术在土地整理复垦开发项目监管工作中的应用实践[J].资源与产业,2009,11(2):66-70.Meng Xiansu,Li Shaoshuai.Application of RS in supervision over land consolidation and rehabilitation[J].Resources&Industries,2009,11(2):66-70.(in Chinese with English abstract)
[10]吴健生,刘建政,黄秀兰,等.基于面向对象分类的土地整理区农田灌排系统自动化识别[J].农业工程学报,2012,28(8):25-31.Wu Jiansheng,Liu Jianzheng,Huang Xiulan,et al.Automatic identification of irrigation and drainage system in landreclamation area based on object-oriented classification[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(8):25-31.(in Chinese with English abstract)
[11]许迪,李益农,蔡林根,等.卫星遥感影像在农田灌排系统识别中的应用研究[J].农业工程学报,2004,20(2):36-39.Xu Di,Li Yinong,Cai Lingen,et al.Applied study of satellite image in identification of irrigation and drainage system[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2004,20(2):36-39.(in Chinese with English abstract)
[12]郭云开,陈正阳,彭悦,等.土地复垦工程的遥感评价[J].遥感技术与应用,2008,23(3):249-254.Guo Yunkai,Chen Zhengyang,Peng Yue,et al.Evaluation of land reclamation based on RS[J].Remote Sensing Technology and Application,2008,23(3):249-254.(in Chinese with English abstract)
[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].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 SVMclassification method[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(2):141-148.(in Chinese with English abstract)
[14]韩文霆,张立元,张海鑫,等.基于无人机遥感与面向对象法的田间渠系分布信息提取[J].农业机械学报,2017,48(3):205-214.Han Wenting,Zhang Liyuan,Zhang Haixin,et al.Extraction method of sublateral canal distribution information based on UAV remote sensing[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):205-214.(in Chinese with English abstract)
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