摘要
高标准农田是国家粮食安全的重要保障,作为其中的重要工程,田间道路的快速准确获取可为高标准农田建设质量评估和效果评价提供基础数据支撑。针对传统方法对细窄田间道路识别精度低、泛化能力不强的问题,本文提出了基于U-Net网络的高标准农田道路识别方法。首先,在分析田间道路基本特征的基础上,选取GF-2影像作为试验数据,采用面向对象方法对影像进行分割并根据对象特征进行分类,剔除光谱特征与田间道路相似的建筑物等非道路要素,减少道路识别干扰;然后,对影像进行裁剪、标签制作和数据增强等操作,并使用U-Net网络挖掘影像的深浅层特征,通过不断调整参数对网络进行训练,实现田间道路的快速识别;最后,依据道路断点特征,采用局部连接法对道路断点进行修复,并以河北省定州市东亭镇为试验区进行方法测算与精度验证。结果表明:通过挖掘622幅田间道路样本的影像特征,U-Net网络可以有效识别各类场景下的高标准农田道路,通过对道路断点进行修复后,研究区田间道路识别精确率达96%,召回率和F1值分别为62%、75%,该识别精度能够满足高标准农田建设质量快速评估要求。相比传统识别方法,结合面向对象和深度学习的方法可以在减少建筑物干扰的基础上快速地识别出田间道路,能更好解决田间道路材质差异大、植被遮挡等造成识别结果噪声多、误识别问题,该方法可为细窄地物的识别提供方法参考。
High-standard farmland construction is an important guarantee for national food security,and the quality assessment of high-standard farmland construction is beneficial to the implementation of farmland planning and government decision-making.As an important project of high-standard farmland construction,the rapid and accurate acquisition of field roads can provide basic data support for the quality assessment and effect evaluation of high-standard farmland construction.Thus,it is necessary to obtain accurate and effective field roads information.However,compared with high-grade roads,the narrow pavement width and easy occlusion by vegetation are the typical characteristics of field roads,which are the main factors leading to the low degree of automation in existing methods.Aiming at the problems of low accuracy and weak generalization ability of traditional recognition methods for narrow field roads,a high-standard farmland road recognition method was proposed based on U-Net network.Firstly,on the basis of analyzing the basic characteristics of the field roads,the GF-2 images were selected as the experimental data,and the object-oriented method was used to segment the image and classify it according to the characteristics of the object,so as to eliminate non-roads such as buildings with similar spectra elements to reduce interference;then,operations such as cropping,labeling,and data enhancement were performed on the image,the U-Net network was used to mine the deep and shallow features of the image,and the network was continuously trained by adjusting parameters to achieve accurate identification of field roads;finally,according to the characteristics of road breakpoints,the local connection method was used to repair the road breakpoints,and the accuracy verification were carried out in Dongting Town,Dingzhou City,Hebei Province as the experimental area.The results showed that by mining the image features of 622 field road samples,the U-Net network could effectively identify high-standard farmland roads in vari
作者
袁翠霞
赵春江
任艳敏
刘玉
李淑华
李少帅
YUAN Cuixia;ZHAO Chunjiang;REN Yanmin;LIU Yu;LI Shuhua;LI Shaoshuai(Research Center of Information Technology,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China;National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China;Land Consolidation and Rehabilitation Center,Ministry of Natural Resources,Beijing 100035,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2023年第5期163-169,218,共8页
Transactions of the Chinese Society for Agricultural Machinery
基金
北京市农林科学院院青年基金项目(QNJJ202232)、北京市农林科学院农业农村部农业遥感机理与定量遥感重点实验室建设项目(PT2023-26)
自然资源部国土卫星遥感应用重点实验室开放基金项目(KLSMNR-202205)。
