| 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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| 引用本文： | 顾铮鸣,金晓斌,杨晓艳,赵庆利,蒋宇超,韩博,单薇,刘晶,周寅康.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[J].农业工程学报,2018,34(23):85-93. |
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| 作者姓名： | 顾铮鸣 金晓斌 杨晓艳 赵庆利 蒋宇超 韩博 单薇 刘晶 周寅康 |
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| 作者单位： | 1. 南京大学地理与海洋科学学院，南京 210023;,1. 南京大学地理与海洋科学学院，南京 210023; 2. 国土资源部海岸带开发与保护重点实验室，南京 210023; 3. 江苏省土地开发整理技术工程中心，南京 210023;,4. 国土资源部土地整治中心，北京 100035;,4. 国土资源部土地整治中心，北京 100035;,1. 南京大学地理与海洋科学学院，南京 210023;,1. 南京大学地理与海洋科学学院，南京 210023;,1. 南京大学地理与海洋科学学院，南京 210023;,1. 南京大学地理与海洋科学学院，南京 210023;,1. 南京大学地理与海洋科学学院，南京 210023; 2. 国土资源部海岸带开发与保护重点实验室，南京 210023; 3. 江苏省土地开发整理技术工程中心，南京 210023; |
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| 基金项目： | 国家科技支撑计划项目（2015BAD06B02） |
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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。
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| 关 键 词： | 无人机 遥感 模型 土地整治 基础设施 |
| 收稿时间： | 2018/6/8 0:00:00 |
| 修稿时间： | 2018/9/19 0:00:00 |
Monitoring roads and canals utilization condition for land consolidation project based on UAV remote sensing image |
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| Gu Zhengming,Jin Xiaobin,Yang Xiaoyan,Zhao Qingli,Jiang Yuchao,Han Bo,Shan Wei,Liu Jing and Zhou Yinkang.Monitoring roads and canals utilization condition for land consolidation project based on UAV remote sensing image[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(23):85-93. |
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| Authors: | Gu Zhengming Jin Xiaobin Yang Xiaoyan Zhao Qingli Jiang Yuchao Han Bo Shan Wei Liu Jing and Zhou Yinkang |
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| Institution: | 1. School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210023, China;,1. School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210023, China; 2. Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resources, Nanjing 210023, China; 3. Jiangsu Land Survey and Planning Institute, Nanjing 210000, China;,4. The Ministry of Land and ResourcesLand Management Center, Beijing 100035, China,4. The Ministry of Land and ResourcesLand Management Center, Beijing 100035, China,1. School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210023, China;,1. School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210023, China;,1. School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210023, China;,1. School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210023, China; and 1. School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210023, China; 2. Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resources, Nanjing 210023, China; 3. Jiangsu Land Survey and Planning Institute, Nanjing 210000, China; |
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| Abstract: | Abstract: 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 2a mainly took pictures on main field roads and canals for precision shooting, and route 2b 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 2a was used to validate the classification accuracy due to the image resolution was higher. Route 2b 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. |
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| Keywords: | unmanned aerial vehicle remote sensing models land consolidation infrastructure |
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