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当前位置: 首页 > 文章 > 基于无人机可见光影像的新疆棉田田间尺度地物识别 农业机械学报 2023,54 (z2) 199-205
Position: Home > Articles > Field Scale Cotton Land Feature Recognition Based on UAV Visible Light Images in Xinjiang Transactions of the Chinese Society for Agricultural Machinery 2023,54 (z2) 199-205

基于无人机可见光影像的新疆棉田田间尺度地物识别

作  者：
张楠楠;张晓;白铁成;袁新涛;马瑞;李莉
单  位：
关键词：
棉田;地物分类;无人机可见光影像;田间尺度;空间分辨率;随机森林
摘  要：
针对无人机采集影像时不同地物最佳分辨率难以确定的问题,运用大疆M600Pro型无人机获取棉花蕾期可见光影像,结合地面调查采样数据,利用神经网络(Artificial neural networks,ANN)、支持向量机(Support vector machines,SVM)和随机森林(Random forest,RF)3种监督分类算法进行田间地物识别.分析不同分辨率(1.00、2.50、5.00、7.50、10.00 cm)下对地物的识别精度,并结合算法运行时间,从分辨率、算法精度和运行时间上找到适合南疆田间尺度棉花田块地物识别的最佳分辨率和最优算法.试验结果表明:当空间分辨率为1.00 cm时,SVM对地物的识别精度最高,总体精度与Kappa系数分别为99.857%和0.997.随着空间分辨率的降低,总体精度和Kappa系数呈下降趋势.当分辨率为2.50 cm和5.00 cm时,采用RF算法,运行时间最短,土地、棉花和滴灌带可获得较好的识别精度,总体精度与Kappa系数分别可达99.252%和0.986以上.当空间分辨率大于5.00 cm时,总体精度和Kappa系数下降,滴灌带制图精度(Producer's accuracy,PA)和用户精度(User's accuracy,UA)下降最大.空间分辨率小于5.00 cm的图像能够很好地识别蕾期棉花地的典型地物,可为进行田间地物类型及其分布状况的识别提供指导.
译  名：
Field Scale Cotton Land Feature Recognition Based on UAV Visible Light Images in Xinjiang
关键词：
cotton%object classification%unmanned aerial vehicle(UAV)visible light images%field scale%ground resolution%random forest
摘  要：
In order to address the challenge of determining optimal resolutions for capturing images of different features using UAVs,the DJI M600Pro UAV was employed to acquire visible light images of cotton fields during the bud stage.By combining ground survey data and utilizing three supervised classification algorithms:artificial neural networks(ANN),support vector machines(SVM),and random forest(RF),field feature identification was conducted.The analysis encompassed varying resolutions(1.00 cm,2.50 cm,5.00 cm,7.50 cm,10.00 cm)to evaluate the accuracy of feature recognition.Additionally,algorithm execution times were considered,with the aim of identifying the best resolution and optimal algorithm for cotton field feature recognition at the field scale in Southern Xinjiang,considering resolution,accuracy,and processing time.Experimental results indicated that at a spatial resolution of 1.00 cm,SVM exhibited the highest accuracy in feature recognition,achieving an overall accuracy of 99.857%and a Kappa coefficient of 0.997.As spatial resolution was decreased,both overall accuracy and Kappa coefficient demonstrated a decreasing trend.At resolutions of 2.50 cm and 5.00 cm,when utilizing the RF algorithm,the shortest execution times were observed.Land,cotton,and drip irrigation lines displayed favorable recognition accuracy,with overall accuracy and Kappa coefficients surpassing 99.137%and 0.983,respectively.With resolutions exceeding 5.00 cm,both overall accuracy and Kappa coefficient declined,notably impacting the mapping accuracy of drip irrigation lines(producer's accuracy,PA)and user accuracy(user's accuracy,UA).Images with resolutions lower than 5.00 cm effectively identified characteristic features of bud-stage cotton fields,offering guidance for the identification of field feature types and their distribution patterns.

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