韩文霆,张立元,张海鑫,师志强,苑梦婵,王紫军.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,SHI Zhiqiang,YUAN Mengchan,WANG Zijun.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.
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基于无人机遥感与面向对象法的田间渠系分布信息提取   [下载全文]
Extraction Method of Sublateral Canal Distribution Information Based on UAV Remote Sensing   [Download Pdf][in English]
投稿时间:2016-07-01  
DOI:10.6041/j.issn.1000-1298.2017.03.026
中文关键词:  灌区渠系分布  信息提取  无人机遥感  多光谱遥感图像  田间毛渠  面向对象法
基金项目:科技部国际合作项目(2014DFG72150)和杨凌示范区工业项目(2015GY-03)
作者单位
韩文霆 西北农林科技大学 
张立元 西北农林科技大学 
张海鑫 西北农林科技大学 
师志强 西北农林科技大学 
苑梦婵 西北农林科技大学 
王紫军 西北农林科技大学 
中文摘要:针对目前农田灌排系统识别研究中遥感影像分辨率不足,难以提取田间毛渠且对无水或少水灌排沟渠识别不足等问题,以内蒙古河套灌区磴口县坝塄村为研究区域,利用固定翼无人机搭载520~920nm多光谱相机进行航拍试验,采用基于面向对象法的特征组合分层分类的提取方法对获取的高分辨率单幅多光谱影像数据进行解译,采用分割阈值为65、合并阈值为90的遥感影像最佳分割参数。利用含水田间毛渠和无水、少水田间毛渠在光谱、几何、空间关系等特征参量中表现出的与其它地物的特异性,建立不同分类层次的规则提取田间毛渠分布信息。提取结果表明,由于水体对近红外波段光谱的强烈吸收,含水毛渠提取效果很好,精度达到97.8%;无水、少水田间毛渠提取精度为75.7%。无人机遥感技术和面向对象法的特征组合分层分类方法为灌区田间渠系识别提供了一种新途径。
HAN Wenting  ZHANG Liyuan  ZHANG Haixin  SHI Zhiqiang  YUAN Mengchan  WANG Zijun
Northwest A&F University,Northwest A&F University,Northwest A&F University,Northwest A&F University,Northwest A&F University and Northwest A&F University
Key Words:distribution of canal system in irrigation districts  information extraction  UAV remote sensing  multispectral remote sensing image  sublateral canal  object oriented method
Abstract:In order to solve the problem that difficult to extract distribution information of sublateral canal without water or with less water caused by low resolution of remote sensing image, a hierarchical classification method of feature combination was proposed, which was based on object-oriented classification method. Bangleng village in Hetao Irrigation District was chosen as the study region, and multi-spectral images were obtained by using fixed-wing unmanned aerial vehicle (UAV) which carried multi-spectrum camera (520~920nm). After a lot of experiments, finally, the segmentation threshold value of 65 and the combined threshold value of 90 were chosen as the best remote sensing image segmentation parameters, then can interpret the obtained high resolution multi-spectral image data. By comparing the spectrum, geometry, spatial relationships between sublateral canal and the other surface features, different levels of classification rules were established to extract sublateral canal distribution information. And 14 sublateral canals in the study region were extracted. The results showed that due to the strong absorption in near infrared spectrum of water, the extraction accuracy of sublateral canal with water was 97.8%;the extraction accuracy of sublateral canal with less water or no water was 75.7%. Using UAV remote sensing techniques and combination of features object-riented hierarchical classification method provided a new way to identify sublateral canal in irrigation area. And future research should focus on eliminating the effect of trees, weeds and gate, as well as extracting canal which in both sides had surface features with close spectrum.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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