| 基于无人机遥感与卷积神经网络的草原物种分类方法 |
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| 引用本文： | 杨红艳,杜健民,王圆,张燕斌,张锡鹏,康拥朝.基于无人机遥感与卷积神经网络的草原物种分类方法[J].农业机械学报,2019,50(4):188-195. |
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| 作者姓名： | 杨红艳 杜健民 王圆 张燕斌 张锡鹏 康拥朝 |
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| 作者单位： | 内蒙古农业大学;内蒙古工业大学,内蒙古农业大学,内蒙古农业大学,内蒙古农业大学,内蒙古农业大学,内蒙古农业大学 |
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| 基金项目： | 国家自然科学基金项目（31660137） |
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| 摘 要： | 基于无人机高光谱成像遥感系统,在400～1 000 nm波段内采集低矮、混杂生长的荒漠草原退化指示物种的高光谱图像信息。分别在退化指示物种的开花期、结实期和黄枯期进行飞行实验,飞行高度30 m,高光谱图像地面分辨率2. 3 cm。采用特征波段提取与深度学习卷积神经网络相结合的方式,提出一种荒漠草原物种水平分类的方法,结合植物物候给出了中国内蒙古中部荒漠草原物种分类的推荐时相,总体分类精度和Kappa系数平均值分别达到94%和0. 91。研究结果表明,无人机高光谱成像遥感技术及深度卷积神经网络可以较好地实现荒漠草原退化指示物种的分类,与基于径向基核函数的支持向量机、基于主成分分析的深度卷积神经网络分类法相比,基于特征波段选择的深度卷积神经网络分类法效果最好,分类精度最高。无人机搭载高光谱成像仪低空遥感和卷积神经网络法提供了一种草原物种水平分类的途径。
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| 关 键 词： | 荒漠草原 指示物种 分类 高光谱遥感 无人机 卷积神经网络 |
| 收稿时间： | 2018/12/13 0:00:00 |
Classification Method of Grassland Species Based on Unmanned Aerial Vehicle Remote Sensing and Convolutional Neural Network |
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| YANG Hongyan,DU Jianmin,WANG Yuan,ZHANG Yanbin,ZHANG Xipeng and KANG Yongchao.Classification Method of Grassland Species Based on Unmanned Aerial Vehicle Remote Sensing and Convolutional Neural Network[J].Transactions of the Chinese Society of Agricultural Machinery,2019,50(4):188-195. |
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| Authors: | YANG Hongyan DU Jianmin WANG Yuan ZHANG Yanbin ZHANG Xipeng and KANG Yongchao |
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| Institution: | Inner Mongolia Agricultural University;Inner Mongolia University of Technology,Inner Mongolia Agricultural University,Inner Mongolia Agricultural University,Inner Mongolia Agricultural University,Inner Mongolia Agricultural University and Inner Mongolia Agricultural University |
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| Abstract: | Grassland degradation is an ecological problem facing the world. Investigating the species composition and species distribution of grassland is extremely important for judging the degradation process of grassland. At present, satellite remote sensing technology is difficult to meet the requirements of grassland species level classification due to the limitation of spatial resolution. Unmanned aerial vehicle (UAV) hyperspectral remote sensing technology provides images of centimeter level spatial resolution and nanoscale spectral resolution required for grassland species classification. Based on the UAV hyperspectral imaging remote sensing system, the hyperspectral image data of low and mixed growth desert grassland degradation indicator species were collected in the 400~1000nm spectral range. Flight experiments were carried out at the flowering, fruiting and yellow blight periods of the degraded indicator species. The flying height was 30m and the ground resolution of the hyperspectral image was about 2.3cm.Based on the combination of feature bands extraction and deep learning convolutional neural network (CNN), a method for classification of desert grassland species was proposed. The recommended phenological phase of species classification of desert grassland in central Inner Mongolia, China, was given in combination with plant phenology. The overall classification accuracy and Kappa coefficient reached 94% and 0.91, respectively. The results showed that the UAV hyperspectral imaging remote sensing technology and deep CNN can better classify the indicator species of desert grassland degradation. Compared with the support vector machine based on radial basis kernel function and the deep CNN based on principal component analysis, the deep CNN classification based on feature bands selection had the best effect and the highest classification accuracy. The method of CNN and the low-altitude remote sensing of UAV equipped with hyperspectral imager provided a new way to classify grassland species. The research result provided characteristic parameters for the judgment of grassland degradation succession process, and quantitative indicators for grassland ecological restoration management. |
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| Keywords: | desert grassland indicator species classification hyperspectral remote sensing unmanned aerial vehicle convolutional neural network |
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