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基于無人機遙感的灌區(qū)土地利用與覆被分類方法
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科技部國際合作項目(2014DFG72150)和楊凌示范區(qū)工業(yè)項目(2015GY-03)


Classification Method of Land Cover and Irrigated Farm Land Use Based on UAV Remote Sensing in Irrigation
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    摘要:

    為研究無人機可見光遙感技術(shù)在灌區(qū)土地利用和覆被分類中的有效性,,以河套灌區(qū)五原縣塔爾湖鎮(zhèn)為試驗區(qū)域,,用TEZ固定翼無人機搭載索尼A5100型相機進行航拍試驗。應(yīng)用Agisoft PhotoScan軟件對無人機遙感系統(tǒng)獲取的可見光高分辨率原始單張影像數(shù)據(jù)進行拼接處理,。除目視提取的特殊用地與水域及水利設(shè)施用地外,,通過試誤法確定分割尺度300、形狀權(quán)重0.4,、緊致度權(quán)重0.5為無人機遙感影像數(shù)據(jù)的最佳分割參數(shù),。通過對剩余各地物在光譜、形狀,、紋理特征參量中表現(xiàn)的特異性,,分別建立決策樹、支持向量機,、K最近鄰分類規(guī)則集提取土地利用類型試驗,。結(jié)果表明,支持向量機能較準確地提取各地物的特征,總體精度為82.20%,,Kappa系數(shù)為0.7659,;決策樹分類方法的總體精度為74.00%,,Kappa系數(shù)為0.6675,;K最近鄰分類方法的總體精度為71.40%,Kappa系數(shù)為0.6107,。采用支持向量機結(jié)合決策樹分類法創(chuàng)建的決策樹模型,,可以將總體精度提高到84.20%,Kappa系數(shù)達到0.7900,。因此無人機可見光遙感技術(shù)可以用于提取灌區(qū)土地利用類型,,但存在農(nóng)、毛渠錯分為交通運輸用地的情況,,渠系的提取還需進一步研究,。

    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.

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韓文霆,郭聰聰,張立元,楊江濤,雷雨,王紫軍.基于無人機遙感的灌區(qū)土地利用與覆被分類方法[J].農(nóng)業(yè)機械學報,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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  • 收稿日期:2016-05-04
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  • 在線發(fā)布日期: 2016-11-10
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