周静平,李存军,史磊刚,史姝,胡海棠,淮贺举.基于决策树和面向对象的作物分布信息遥感提取[J].农业机械学报,2016,47(9):318-326.
Zhou Jingping,Li Cunjun,Shi Leigang,Shi Shu,Hu Haitang,Huai Heju.Crops Distribution Information Extracted by Remote Sensing Based on Decision Tree and Object-oriented Method[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(9):318-326.
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基于决策树和面向对象的作物分布信息遥感提取   [下载全文]
Crops Distribution Information Extracted by Remote Sensing Based on Decision Tree and Object-oriented Method   [Download Pdf][in English]
投稿时间:2016-05-17  
DOI:10.6041/j.issn.1000-1298.2016.09.043
中文关键词:  遥感  作物分布; 信息提取; 决策树  面向对象
基金项目:国家自然科学基金项目(41171281)
作者单位
周静平 北京农业信息技术研究中心 
李存军 北京农业信息技术研究中心
北京智慧农业物联网产业技术创新战略联盟 
史磊刚 北京农业信息技术研究中心 
史姝 四川省第三测绘工程院 
胡海棠 北京农业信息技术研究中心 
淮贺举 北京农业信息技术研究中心 
中文摘要:利用我国2012年4—11月覆盖主要农作物全生育期的23幅中分辨率HJ-1A/1B卫星时序影像,采用决策树和面向对象相结合的分类方法提取黑龙江省双河农场主要农作物分布信息,并与传统决策树分类方法进行对比。通过影像预处理构建时序HJ星影像集,先利用面向对象方法提取道路,为作物提取排除田间道路及附属地物干扰;再结合作物物候历分析不同地物光谱和时序特征,筛选出7个特征指数和14个敏感时相,建立决策树分类模型,提取出玉米和水稻。研究表明,多特征指数辅助作物分类十分有效,尤其是归一化水指数NDWI对水稻提取非常有效;较之传统决策树分类,决策树和面向对象相结合的分类方法能有效剔除田间道路及附属林带沟渠对作物分类的干扰,总体分类精度从89.22%提升至95.18%,该方法可为其他地区利用中分辨率遥感影像低成本高精度提取作物分布信息提供借鉴。
Zhou Jingping  Li Cunjun  Shi Leigang  Shi Shu  Hu Haitang  Huai Heju
Beijing Research Center for Information Technology in Agriculture,Beijing Research Center for Information Technology in Agriculture;Beijing Technology Innovation Strategic Alliance for Intelligence Internet of Things Industry in Agriculture,Beijing Research Center for Information Technology in Agriculture,The Third Surveying and Mapping Engineering Institute of Sichuan,Beijing Research Center for Information Technology in Agriculture and Beijing Research Center for Information Technology in Agriculture
Key Words:remote sensing  crops distribution  information extraction  decision tree  object oriented
Abstract:Accurately acquiring crops distribution information is of great significance for agricultural production management and yield estimation, but the roads, forest belts and ditches in the farmland seriously affect the accuracy of crops classification and extraction. Chinese small satellite constellation of small satellites for environment and disaster monitoring and forecasting (HJ-1A/1B satellite) is a good data source for crops classification, because it is free for researchers and has a higher spatial resolution of 30m and a higher time resolution of two days. In this paper, Shuanghe farm in Heilongjiang province of China was the research area, 23 time series HJ-1A/1B images which cover the growth period of the major crops from April 3th to November 9th, 2012, were used to monitor the roads and forest belts in the farm, extract spatial distribution of the major crops based on decision tree and object oriented method, and the classification result was compared to traditional decision tree. The time series image set and the time series characteristic index set such as NDVI, DVI, RVI, EVI and NDWI were built after the original image data pretreatment. Firstly, the road in the farm was extracted with object oriented classification based on elements of length width ratio and other parameters, then the time series set was masked by the road in order to rule out the interference of roads, forest belts and ditches for the extraction of crops information. Secondly, seven effective characteristic parameters and 14 sensitive time phases were chosen by using the object spectrum, time phase and time series characteristics. The thresholds of characteristic parameters were determined, and the decision tree classification model of major crops was established. Finally, the major crops in Shuanghe farm such as corn and rice were extracted. The result showed that using many characteristic indices to classify crops was very effective, and especially NDWI was very helpful for rice extraction. The method of decision tree and object oriented classification was better than the traditional decision tree for extracting the spatial distribution of major crops in Shuanghe farm, it could effectively eliminate the interference of roads, forest belts and ditches in the farm for crops classification, and the total accuracy was increased from 89.22% to 95.18%. The integration of decision tree and object oriented classification can provide reference for crops distribution information extraction in other agricultural areas with low cost and high precision.

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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