光谱学与光谱分析, 2020, 40 (5): 1356, 网络出版: 2020-12-09  

无人机光谱成像技术在大田中的应用研究进展

Research Progress in the Application of UAV Spectral Imaging Technology in Field
作者单位
1 中国农业大学工学院食品质量与安全北京实验室, 北京 100083
2 中国农业大学土地科学与技术学院, 北京 100083
3 中国农业大学工学院, 北京 100083
4 中国农业大学水利与土木工程学院, 北京 100083
5 中国农业大学信息与电气工程学院, 北京 100083
摘要
传统大田农作物生长环境监测的方法, 需要在环境恶劣的田间布设各种传感器, 铺设复杂电路, 通常会出现耗时费力、 维护成本高、 且或多或少的损坏到植株的问题。 无人机光谱成像技术是一种融合了无人驾驶飞行器技术、 空间遥感和图像实时传输等多种手段的快捷新型农田环境监测技术, 能够快速获取农田作物的即时光谱图像, 通过分析图像获取大田作物的生长信息, 这一技术的应用弥补了上述问题。 首先对无人机光谱成像技术进行了概述, 介绍了无人机应用的优势。 和传统卫星遥感监测平台相比, 无人机可以工作在较低的高度, 即80~400 m, 能够抵消极端天气和云层影响, 实现快速、 准确地获取高精度图像。 目前, 国内外小型无人机的应用主要集中在灾害监测、 自然资源监测、 城市规划和植被监测等领域。 由于其低成本、 近实时图像采集等特点, 在精准农业的发展过程中, 无人机光谱图像的应用也愈加广泛。 分析了常见光谱图像的特点和应用场景。 全色图像由于分辨率高, 多用于数据融合; 多光谱及高光谱影像由于丰富的光谱信息, 与农作物的光谱特征结合, 可用于农作物的生物理化指标的检测、 农业灾害预警、 产量预测和精细分类制图等; 热红外图像可以获得农作物温度信息, 可用于监测田间旱灾。 总结了无人机光谱图像技术在大田中的主要应用途径。 目前利用无人机光谱图像技术对农作物进行监测的方法主要有: 利用光谱反射率构造植被指数或红边参数, 研究植被的反射特点, 构建农作物时间层面上与光谱特征对应的生长模型, 利用新兴数学方法与农作物生化参数结合建立模型进行反演。 探讨了无人机光谱图像技术在大田的应用中尚且存在着的一些技术空白及难点, 以期为无人机光谱成像技术在大田中的衍生应用发展提供参考。
Abstract
Traditional methods of crop monitoring in the field need to lay various sensors and complex circuits in the field with a bad environment. Usually, the problems of time-consuming, labor-consuming, high maintenance cost and more or less damage to plants arise. Unmanned aerial vehicle (UAV) spectral imaging technology is a new and fast technology for monitoring farmland environment, which combines an unmanned aerial vehicle (UAV), remote sensing sensors, real-time image transmission and other means. It can quickly obtain real-time spectral images of farmland crops. Usually, it can analyze images to obtain the growth information of farmland crops. The application of this technology catches up with the above problems. Firstly, the spectrum imaging technology of UAV is summarized, and the advantages of UAV application are introduced. Compared with traditional satellite remote sensing monitoring platform, UAV can work at a lower altitude, i. e. 80~400 m. It can resist the disadvantage of adverse weather and clouds, and achieve fast and accurate acquisition of high-precision images. At present, the application of small UAVs at home and abroad mainly focuses on disaster monitoring, natural resources monitoring, urban planning and vegetation monitoring. In addition, due to its low cost, near real-time image acquisition and other characteristics, in the development of precision agriculture, unmanned aerial vehicle (UAV) spectral images are more commonly used. Secondly, the characteristics and application scenarios of common spectral images are analyzed. Panchromatic images are mostly used for data fusion because of their high resolution; multispectral and hypersecretion images are combined with spectral characteristics of crops due to their abundant spectral information, which can be used for the detection of biological and chemical indicators of crops, early warning of agricultural disasters, yield prediction and fine classification mapping; and thermal infrared images can be used for monitoring field drought because they can obtain crop temperature information. Then the main application ways of UAV spectral image technology in the field are summarized. At present, the main methods of monitoring crops using UAV spectral image technology are: using spectral reflectance to construct vegetation index or red edge parameters, or studying the reflection characteristics of vegetation, constructing crop growth model, using multiple linear regression, partial least squares method, in-depth learning and other biochemical parameters of crops to establish a model for inversion. Finally, shortcomings of UAV spectral imaging technology in the field application are discussed, and the future development prospects of this new technology have prospected, in order to provide a reference for the derivative application of UAV spectral imaging technology in the field.

彭要奇, 肖颖欣, 郑永军, 严海军, 董玉红, 李鑫星. 无人机光谱成像技术在大田中的应用研究进展[J]. 光谱学与光谱分析, 2020, 40(5): 1356. PENG Yao-qi, XIAO Ying-xin, ZHENG Yong-jun, YAN Hai-jun, DONG Yu-hong, LI Xin-xing. Research Progress in the Application of UAV Spectral Imaging Technology in Field[J]. Spectroscopy and Spectral Analysis, 2020, 40(5): 1356.

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