韩文霆,郭聪聪,张立元,杨江涛,雷雨,王紫军.基于无人机遥感的灌区土地利用与覆被分类方法[J].农业机械学报,2016,47(11):270-277.
Han Wenting,Guo Congcong,Zhang Liyuan,Yang Jiangtao,Lei Yu,Wang Zijun.Classification Method of Land Cover and Irrigated Farm Land Use Based on UAV Remote Sensing in Irrigation[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(11):270-277.
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基于无人机遥感的灌区土地利用与覆被分类方法   [下载全文]
Classification Method of Land Cover and Irrigated Farm Land Use Based on UAV Remote Sensing in Irrigation   [Download Pdf][in English]
投稿时间:2016-05-04  
DOI:10.6041/j.issn.1000-1298.2016.11.037
中文关键词:  无人机遥感  可见光波段  灌区土地利用  土地覆被分类  支持向量机
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 
郭聪聪 西北农林科技大学 
张立元 西北农林科技大学 
杨江涛 西北农林科技大学 
雷雨 西北农林科技大学 
王紫军 西北农林科技大学 
中文摘要:为研究无人机可见光遥感技术在灌区土地利用和覆被分类中的有效性,以河套灌区五原县塔尔湖镇为试验区域,用TEZ固定翼无人机搭载索尼A5100型相机进行航拍试验。应用Agisoft PhotoScan软件对无人机遥感系统获取的可见光高分辨率原始单张影像数据进行拼接处理。除目视提取的特殊用地与水域及水利设施用地外,通过试误法确定分割尺度300、形状权重0.4、紧致度权重0.5为无人机遥感影像数据的最佳分割参数。通过对剩余各地物在光谱、形状、纹理特征参量中表现的特异性,分别建立决策树、支持向量机、K 最近邻分类规则集提取土地利用类型试验。结果表明,支持向量机能较准确地提取各地物的特征,总体精度为82.20%,Kappa系数为0.7659;决策树分类方法的总体精度为74.00%,Kappa系数为0.6675;K 最近邻分类方法的总体精度为71.40%,Kappa系数为0.6107。采用支持向量机结合决策树分类法创建的决策树模型,可以将总体精度提高到84.20%,Kappa系数达到0.7900。因此无人机可见光遥感技术可以用于提取灌区土地利用类型,但存在农、毛渠错分为交通运输用地的情况,渠系的提取还需进一步研究。
Han Wenting  Guo Congcong  Zhang Liyuan  Yang Jiangtao  Lei Yu  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:UAV remote sensing  visible band  irrigated farm land use  land cover classification  SVM
Abstract:In order to verify the availability of UAV(unmanned aerial vehicle) optical remote sensing technology in land use type and classification, Wuyuan county Tal Lake town of Hetao Irrigation Area was chosen as research area and visible images were obtained by using TEZ fixed wing UAV equipment with SONY A5100. After obtaining the visible high resolution images by using the UAV remote sensing system, they were mosaicked in the Agisoft PhotoScan software. In addition to visually extracting ground object, we also adopted object oriented which segmentation scale was 300, shape factor was 0.4, smoothness was 0.5 to divide images. On the basis of visual, according to the specificity of ground object in spectrum, shape and texture feature, we respectively established decision tree, support vector machine, K nearest neighbor classification to extract land use type. Results indicated that SVM can accurately extract characteristics of ground object, the overall accuracy was 82.20%, Kappa coefficient was 0.7659; overall accuracy and Kappa coefficient of decision tree were 74.00% and 0.6675, respectively; overall accuracy and Kappa coefficient of K nearest neighbor classification were 71.40% and 0.6107, respectively.4 In this paper, based on the support vector machine classification method combined with the decision tree model, the overall accuracy was grown up to 84.20%, Kappa coefficient reached 0.7900. But there existed the wrong situation of small trench being divided into traffic and transport. The visible UAV remote sensing technology can be used to extract the irrigated land use types, but the extraction ditches need further study.

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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