摘要：高光谱遥感图像具有很高的光谱分辨率，能够提供丰富的地物细节，有助于物理化学特性反演。在遥感应用领域中，植被指数被应用于定性和定量评估植被覆盖度，以及评估植物生长状况。对植被指数进行分类，以及分类后评价其各自优势和局限性，将有助于遥感在农业，植被和生态环境监测中的有效开发和应用，这对植被研究和农业发展具有重要意义。

论文通过对高光谱图像植被的吸收和反射特征进行研究，开展了不同植被指数的自动计算，并可以正确显示植被覆盖区域并携带对作物作物的分析可视化表明。主要工作为：

（1）对图像点进行归一化反射率处理，运用归一化之后的光谱值，研究植被指数的提取方法，通过6种不同的植被指数计算公式，对图像中每一点进行植被指数的计算，作图分析植被指数分布情况。

（2）读取植被指数数据，根据该数据对原始灰度图进行彩虹条着色，以反映植被指数在图中各点所代表地区的分布及含量情况，对图像中植被指数含量最高点拟合光谱曲线。

关键词  高光谱图像 植被指数

毕业设计说明书外文摘要

Title  Extraction of vegetation index in hyperspectral  image and its software implementation

Abstract：Hyperspectral remote sensing images have a high spectral resolution and can provide rich detail of the features that contribute to the inversion of physical and chemical properties. In remote sensing applications, vegetation indices are used to qualitatively and quantitatively assess vegetation coverage and to assess plant growth. The classification of vegetation indices and the evaluation of their respective advantages and limitations after classification will contribute to the effective development and application of remote sensing in agriculture, vegetation and ecological environment monitoring, which is of great significance to vegetation research and agricultural development.

Based on the study of the absorption and reflection characteristics of hyperspectral image vegetation, the automatic calculation of different vegetation indices is carried out, and the vegetation coverage area can be displayed correctly and the visualization of crop crops is carried out. The main work is:

(1) The normalized reflectivity of the image points was studied. The vegetation index was calculated by using the normalized spectral values. The vegetation index was calculated for each point in the image by six different vegetation index formulas. , Map analysis of vegetation index distribution.

(2) Reading the vegetation index data, according to the data on the original gray scale coloring rainbow to reflect the vegetation index in the map of the distribution of the area and the content of the situation, the highest index of vegetation index Spectral curve..

Keywords  Hyperspectral image；Vegetation index

目   次

1  引言 1

1.1  工程背景及意义 1

1.2  相关技术的现状 1

1.3  总体技术方案及其社会影响 2

1.4  技术方案的经济因素分析 3

1.5  论文章节安排 3

2  高光谱图像与高光谱遥感技术 4

2.1  高光谱图像基本原理 4

2.2  高光谱图像传感器 4

2.3  高光谱成像与多光谱成像的区别 5

2.4  高光谱数据采集技术 5

2.5  高光谱成像的优缺点 6

3  高光谱遥感与植被指数 7

3.1  植被指数的概念 7

3.2  归一化植被指数NDVI 7

3.3  NDVI的计算及其基本原理 8

3.4  NDVI的性能极其局限性 8

4  高光谱植被指数提取实现方法 10

4.1  Qt技术简介 10

4.2  需求分析 11

4.3  软件功能模块及其设计 11

4.4  软件实现与实验结果 19