【作者】 王劼；

【导师】 吴刚；

【作者基本信息】 中国科学技术大学 ， 控制理论与控制工程， 2011， 硕士

【摘要】 本文以国家高技术研究发展计划(863)“田间作物信息成像光谱仪的研制与应用”(2007AA10Z202)、“基于国产旋翼无人机的农业低空高光谱遥感技术”(2010AA10Z201)为研究背景,采用自主研制的“Offner田野成像光谱仪”,介绍了成像光谱分析的方法及流程,对大田冬小麦叶片进行成像光谱采集实验,并结合实验数据进行小麦叶绿素定量分析研究。论文主要内容包括以下几部分:一、成像光谱技术用于小麦叶绿素含量无损检测的意义,成像光谱技术的发展历史,成像光谱技术在精准农业领域的应用。二、成像光谱分析的光谱预处理方法,最优波长选择方法,常用的多元校正算法原理。三、基于Offner田野成像光谱仪的大田冬小麦叶片成像光谱实验。介绍了实验设计方案,成像光谱仪的主要技术参数,实际田间操作成像光谱系统获取小麦叶片光谱图像的采集、处理方法。四、基于成像光谱数据,建立了叶绿素a、叶绿素b、叶绿素总量、SPAD的偏最小二乘法回归模型。比较不同光谱预处理方法对模型的影响,从提高建模效率、减少建模变量的角度,研究了连续投影算法在高光谱建模中的作用,并与光谱全波段建立的偏最小二乘回归模型做了比较分析。五、基于成像光谱数据的叶绿素检测模块的设计与实现。模块包含了成像光谱定量分析流程中的基本数据处理和分析方法。使用这个叶绿素检测工具模块,将使高光谱数据分析处理更加方便、准确,为从高光谱数据中提取信息提供了一个快速、便捷的基础性工具。六、本文工作的创新点是使用Offner田野成像光谱仪对田间冬小麦进行地面观测实验,获取冬小麦的图谱信息,研究了成像光谱仪的定量分析方法。设计并实现了Offner田野成像光谱仪的叶绿素检测模块。更多还原

【Abstract】 By using the field Offner imaging spectrometer which developed by the National High Technology Research and Development Program (863)“Development and application of information field crops imaging spectrometer”(2007AA10Z202)and“Agriculture based on domestic rotary wing UAV using low-altitude hyperspectral remote sensing technology”(2010AA10Z201) to collect the spectral and spatial information of winter wheat leaves. First describes the process of imaging spectroscopy, and then studies quantitative analysis of the chlorophyll content in wheat leaves. The main works are as follows:(1) Describes the meaning of imaging spectrometer technology for nondestructive testing of wheat chlorophyll content, history of imaging spectroscopy, and its application in agriculture.(2) Introduces the preprocessing methods, the optimal wavelength selection methods, and the commonly used multivariate calibration algorithm principle, and their advantages and disadvantages.(3) The experiments for field imaging spectroscopy of winter wheat leaves based on the field Offner imaging spectrometer. Introduces the design of experiments and specifications of the imaging spectrometer, actual operation of imaging spectrometer system for acquiring the spectral image of wheat leaves, and data processing method.(4) Based on the imaging spectral data, the partial least squares regression models of the chlorophyll a, chlorophyll b, total chlorophyll, and SPAD are established. Discusses the effect of different spectral preprocessing methods on the accuracy of the emergent model. To improve modeling efficiency and reduce modeling variables, successive projection method is introduced as the preprocessing method in hyper-spectral modeling, and compared with partial least squares regression model of full-band spectral set.(5) Design and implementation of the chlorophyll detection module, which contains the basic data processing and analysis methods in basic quantitative analysis process. The module in Chlorophyll detection will enable hyper-spectral data analysis more convenient and accurate, and provides a quick and convenient tool for extracting information from hyper-spectral data. (6) Innovation of this work is to use the field Offner imaging spectrometer for ground observations of winter wheat in the field experiment, get hyperspectral images information of winter wheat and study the quantitative analysis of imaging spectrometer method. Design and implementation of chlorophyll content detection module based on the field Offner imaging spectrometer.更多还原